A Real Estate Registration Privacy Protection Method and System Based on Zero-Knowledge Proof

Abstract

This application provides a real estate registration privacy protection method and system based on zero-knowledge proof. Among them, first, multi-source sensors are used to collect logistics trajectory data and convert it into a physical carrier for the transfer of property rights certificates, while generating a spatio-temporal coding sequence. In a closed space, dynamic voiceprint parameters are obtained through the acoustic resonance effect, and combined with the spatio-temporal coding sequence to encrypt and generate an encryption key that changes with the logistics path. After the geographical coordinates are bound to the voiceprint parameters, a joint fingerprint of the logistics trajectory and the voiceprint feature is created based on the timestamp condition. The joint fingerprint is split into trajectory shards and voiceprint shards, and recombined under specific time constraint conditions to ensure the consistency of real estate registration data, while isolating the complete path of the spatio-temporal coding sequence and the plaintext information of the encryption key. The technical solution provided by this application can improve the efficiency and security of real estate registration privacy protection.

Description

Technical Field

[0001] This application relates to the technical field of real estate registration privacy protection, and particularly to a real estate registration privacy protection method and system based on zero-knowledge proof. Background Art

[0002] In the process of real estate registration, ensuring the accuracy, integrity, and privacy protection of data is a crucial technical requirement. Especially in logistics transportation, when it comes to the physical carrier of warrant transfer, how to effectively collect and verify the authenticity of logistics track data has become a major challenge. To prevent forgery or tampering, a technology that can dynamically track the logistics process is needed, while ensuring the privacy and security of all participating parties. In addition, this technology must also be able to adapt to different environmental conditions and have the ability to efficiently process large-scale data to support real-time verification and update.

[0003] Currently, real estate registration privacy protection mainly uses blockchain technology to record and verify logistics information. By recording logistics track data on an immutable blockchain, the authenticity and integrity of the data can be effectively guaranteed. At the same time, with the help of smart contracts to automatically execute preset rules, the automation monitoring and verification of the logistics process can be achieved. This method not only improves efficiency, reduces human intervention, but also enhances system transparency and security, providing a reliable data basis for real estate registration.

[0004] However, although blockchain technology provides an effective solution, it also has certain limitations. First, once the data on the blockchain is written, it is difficult to modify, which brings inconvenience to the correction of incorrect data. Second, with the continuous growth of logistics data volume, the blockchain network may face scalability challenges, resulting in a decline in transaction speed and an increase in costs. Finally, although the blockchain itself provides relatively high security, there are still deficiencies in user identity verification and data privacy protection, especially in the real estate registration scenario involving sensitive information, which may lead to the risk of privacy leakage. Summary of the Invention

[0005] This application provides a real estate registration privacy protection method and system based on zero-knowledge proof to solve the problems of low efficiency and poor security in real estate registration privacy protection in the prior art.

[0006] In a first aspect, this application provides a real estate registration privacy protection method based on zero-knowledge proof, including:

[0007] Collect logistics track data through multi-source sensors of the transportation carrier, encapsulate the logistics track data into the physical carrier of warrant transfer, and generate a spatio-temporal coding sequence;

[0008] Capture dynamic voiceprint parameters based on the acoustic resonance effect of the enclosed space of the transportation carrier, synchronously encrypt the dynamic voiceprint parameters with the spatio-temporal nodes of the spatio-temporal coding sequence, and generate an encryption key that dynamically evolves along the logistics path;

[0009] Bind the geographical coordinates of the spatio-temporal coding sequence to the voiceprint parameters of the encryption key, and generate a joint fingerprint of the logistics trajectory and voiceprint features based on the timestamp constraint conditions;

[0010] Split the joint fingerprint into a trajectory shard and a voiceprint shard, perform logical relevance verification on the trajectory shard and the voiceprint shard through the zero-knowledge proof protocol and secure multi-party computation, reorganize the trajectory shard and the voiceprint shard only when the timestamp constraint conditions are met to verify the consistency of the real estate registration data, and isolate the complete path of the spatio-temporal coding sequence and the plaintext parameters of the encryption key.

[0011] Optionally, the generating a joint fingerprint of the logistics trajectory and voiceprint features based on the timestamp constraint conditions includes:

[0012] Divide the geographical coordinates of the spatio-temporal coding sequence into discrete geographical blocks according to the continuous timestamps of the logistics path, and each geographical block corresponds to the displacement range of the transportation carrier within a fixed time interval;

[0013] Generate a voiceprint confusion factor synchronized with the geographical block based on the phase fluctuation of the voiceprint parameters of the encryption key within the corresponding time interval;

[0014] Mix the longitude and latitude coordinates of the geographical block with the voiceprint confusion factor, and interweave the coordinate values, the voiceprint confusion factor, and the corresponding timestamp of the geographical block based on the chaos rule to generate a block confusion unit;

[0015] According to the continuity constraint of the logistics path, perform hash chain association on the timestamp of the block confusion unit and the voiceprint confusion factor to generate a joint fingerprint of the logistics trajectory and voiceprint features.

[0016] Optionally, the mixing the longitude and latitude coordinates of the geographical block with the voiceprint confusion factor, and interweaving the coordinate values, the voiceprint confusion factor, and the corresponding timestamp of the geographical block based on the chaos rule to generate a block confusion unit includes:

[0017] Decompose the longitude and latitude coordinates of the geographical block into a longitude sequence and a latitude sequence, and generate a coordinate permutation sequence based on the enclosed space parameters of the transportation carrier;

[0018] Extract the frequency domain features and time domain periods of the voiceprint confusion factor according to the displacement range of the logistics path corresponding to the timestamp of the time window associated with the geographical block, and generate an interweaving weight vector;

[0019] Perform bit-order offset on the longitude sequence, latitude sequence and the interleaving weight vector, and perform iterative permutation on the offset coordinate components and the voiceprint confusion factor based on the chaotic initial perturbation value to generate a coordinate voiceprint interleaving vector;

[0020] Dynamically perturb the coordinate voiceprint interleaving vector according to the movement direction of the transport carrier, and cross-overlap the perturbed coordinate voiceprint interleaving vector with the coordinate permutation sequence to generate a block confusion unit.

[0021] Optionally, the performing iterative permutation on the offset coordinate components and the voiceprint confusion factor based on the chaotic initial perturbation value to generate a coordinate voiceprint interleaving vector includes:

[0022] Generate a dynamic chaotic seed associated with the chaotic initial perturbation value based on the resonance frequency of the enclosed space of the transport carrier and the displacement range of the logistics path;

[0023] Arrange the offset coordinate components in timestamp order as an input vector, and inject the dynamic chaotic seed into the head and tail nodes of the input vector to generate a chaotic iteration input queue;

[0024] Obtain the iteration step size of the chaotic mapping according to the real-time acceleration and vibration intensity of the transport carrier, and perform cyclic displacement and parameter replacement on the vector elements of the chaotic iteration input queue to generate a chaotic perturbation intermediate vector;

[0025] Intercept the chaotic perturbation intermediate vector according to the logistics path segments corresponding to the timestamps, and perform cross-recombination on the longitude sequence, latitude sequence and the voiceprint confusion factor to generate a coordinate voiceprint interleaving vector.

[0026] Optionally, the performing logical relevance verification on the trajectory shards and voiceprint shards through the zero-knowledge proof protocol and secure multi-party computation includes:

[0027] Apply the speed change parameter of the transport carrier and the resonance frequency parameter of the enclosed space to the trajectory shards and voiceprint shards respectively to generate a trajectory challenge vector and a voiceprint challenge vector;

[0028] Split the trajectory challenge vector and the voiceprint challenge vector into verification shard groups according to the constraint conditions of the timestamps, and distribute the confused trajectory shards and voiceprint shards to different registration agencies;

[0029] Generate a response generation rule based on the real-time state parameters of the transport carrier, and synchronize the response generation rule among the registration agencies through secure multi-party computation;

[0030] In the zero - knowledge proof protocol, perform segmented perturbation on the verification shard group according to the response generation rule to obtain local response shards, where the local response shards contain timestamp continuity identifiers;

[0031] Perform cross - verification on the local response shards. Only when the identifier satisfies timestamp continuity and is consistent with the response generation rule, trigger shard recombination to verify the consistency of real - estate registration data.

[0032] Optionally, the generating the response generation rule based on the real - time status parameters of the transportation carrier includes:

[0033] Divide the real - time speed, acceleration, and vibration intensity of the enclosed space of the transportation carrier into dynamic state sequences according to timestamps, and extract the state parameter components corresponding to each timestamp;

[0034] Based on the amplitude fluctuation of the dynamic state sequence and the frequency - domain offset of the voiceprint confusion factor, generate a dynamic weight factor bound to the timestamp, where the generation of the dynamic weight factor is constrained by the curvature radius of the logistics path;

[0035] Interleave the dynamic weight factor with the trajectory shards and voiceprint shards corresponding to the timestamps to generate a rule template containing timestamp constraint conditions;

[0036] Encrypt the rule template in shards through secure multi - party computation, distribute the encrypted rule shards to the registration agency, and generate a response generation rule based on multi - party collaborative decryption.

[0037] Optionally, the synchronously encrypting the dynamic voiceprint parameters with the spatio - temporal nodes of the spatio - temporal coding sequence to generate an encryption key that dynamically evolves along the logistics path includes:

[0038] Perform phase - fluctuation analysis on the dynamic voiceprint parameters, extract the phase - offset sequence corresponding to its main - frequency component, and quantify the integer sequence that matches the bit - width of the spatio - temporal node timestamp;

[0039] Interleave the longitude and latitude coordinates of the spatio - temporal coding sequence with the timestamp to generate a mixed - coding block, and alternately flip the bits of the mixed - coding block based on the parity of the phase - offset sequence to generate an initial key segment;

[0040] Generate a chaotic perturbation vector based on the hash chain of consecutive timestamps of the logistics path, and perform modulo addition operation on the initial key segment and the chaotic perturbation vector bit - by - bit to generate an intermediate key segment with dynamic characteristics of the logistics path;

[0041] Perform multiple rounds of displacement perturbation on the intermediate key segment according to the voiceprint main - frequency energy decay curve to generate an encryption key that dynamically evolves along the logistics path.

[0042] Second aspect, the present application provides a real estate registration privacy protection system based on zero-knowledge proof, including:

[0043] A collection module that collects logistics track data through multi-source sensors of a transportation carrier, encapsulates the logistics track data into a physical carrier for warrant transfer, and generates a spatio-temporal coding sequence;

[0044] An encryption module that captures dynamic voiceprint parameters based on the acoustic resonance effect in the enclosed space of the transportation carrier, synchronously encrypts the dynamic voiceprint parameters with the spatio-temporal nodes of the spatio-temporal coding sequence, and generates an encryption key that dynamically evolves along the logistics path;

[0045] A generation module that binds the geographical coordinates of the spatio-temporal coding sequence with the voiceprint parameters of the encryption key, and generates a joint fingerprint of the logistics track and the voiceprint feature based on the timestamp constraint condition;

[0046] A verification module that splits the joint fingerprint into a track fragment and a voiceprint fragment, performs logical relevance verification on the track fragment and the voiceprint fragment through a zero-knowledge proof protocol and secure multi-party computation, recombines the track fragment and the voiceprint fragment only when the timestamp constraint condition is satisfied to verify the consistency of the real estate registration data, and isolates the complete path of the spatio-temporal coding sequence and the plaintext parameters of the encryption key.

[0047] Third aspect, the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a real estate registration privacy protection method based on zero-knowledge proof as described in the first aspect above.

[0048] Fourth aspect, the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a real estate registration privacy protection method based on zero-knowledge proof as described in the first aspect.

[0049] In the embodiments of the present application, logistics trajectory data is collected through multi-source sensors of a transportation carrier, the logistics trajectory data is encapsulated into a physical carrier for warrant transfer, and a spatio-temporal coding sequence is generated; dynamic voiceprint parameters are captured based on the acoustic resonance effect in the enclosed space of the transportation carrier, the dynamic voiceprint parameters are synchronously encrypted with the spatio-temporal nodes of the spatio-temporal coding sequence to generate an encryption key that dynamically evolves along with the logistics path; the geographical coordinates of the spatio-temporal coding sequence are bound to the voiceprint parameters of the encryption key, and a joint fingerprint of the logistics trajectory and the voiceprint feature is generated based on the timestamp constraint condition; the joint fingerprint is split into a trajectory fragment and a voiceprint fragment, and the logical relevance verification is performed on the trajectory fragment and the voiceprint fragment through a zero-knowledge proof protocol and secure multi-party computation, and the trajectory fragment and the voiceprint fragment are recombined only when the timestamp constraint condition is satisfied to verify the consistency of the real estate registration data, and the complete path of the spatio-temporal coding sequence and the plaintext parameters of the encryption key are isolated.

[0050] The technical solution of the present application has the following beneficial effects:

[0051] The present application collects logistics information through multi-source sensors and converts it into a physical carrier for warrant transfer, while generating a spatio-temporal coding sequence. This step realizes the accurate capture and standardized processing of data in the logistics process, providing a basis for subsequent encryption and verification. The dynamic voiceprint parameters are obtained by using the acoustic resonance effect in the transportation carrier and are synchronously encrypted in combination with the spatio-temporal coding sequence to generate an encryption key that evolves with the change of the logistics path. This process enhances the security of the encryption key, making it difficult to be predicted or cracked. The geographical coordinates are bound to the voiceprint parameters in the encryption key, and a joint fingerprint is generated according to the timestamp condition. This operation ensures the uniqueness and immutability of the logistics trajectory and the voiceprint feature, further strengthening the data security protection. The joint fingerprint is split into a trajectory fragment and a voiceprint fragment, and the logical relevance verification is carried out through a zero-knowledge proof protocol and secure multi-party computation. This approach maximally protects privacy and avoids the leakage of sensitive information while ensuring the accuracy of verification.

[0052] Further, based on the timestamp constraint condition, the method first divides the geographical coordinates of the spatio-temporal coding sequence into discrete geographical blocks according to the continuous timestamps on the logistics path, and generates a voiceprint confusion factor based on the phase fluctuation of the voiceprint parameters of the encryption key within each time interval. Then, the longitude and latitude coordinates are aliased with the voiceprint confusion factor and interwoven into block confusion units according to the chaotic rules. Finally, according to the continuity requirement of the logistics path, the timestamps and voiceprint confusion factors of the block confusion units are associated through hash chaining to form a joint fingerprint of the logistics trajectory and the voiceprint feature. This method not only improves the complexity and security of the logistics trajectory data and the voiceprint feature data, but also ensures that even if part of the data is intercepted, it is difficult for attackers to interpret the original information or forge a legitimate joint fingerprint. In addition, it effectively maintains the integrity and consistency of the data, and at the same time supports an efficient and privacy-friendly verification mechanism.

[0053] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 Shows a flowchart of a real estate registration privacy protection method based on zero-knowledge proof provided by the present application;

[0056] Figure 2 Shows a schematic structural diagram of a real estate registration privacy protection system based on zero-knowledge proof provided by the present application;

[0057] Figure 3 Shows a schematic structural diagram of a computing device provided by the present application. Detailed Embodiments

[0058] In order to enable those skilled in the art to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application.

[0059] In some of the processes described in the specification, claims, and the above-mentioned drawings of this application, a number of operations appear in a specific order. However, it should be clearly understood that these operations can be executed not in the order in which they appear herein or in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any order of execution. Additionally, these processes can include more or fewer operations, and these operations can be executed sequentially or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0060] This solution proposes a method for collecting logistics trajectory data based on multi-source sensors. By encapsulating this data into physical carriers and generating spatio-temporal coding sequences, it supports privacy protection in the process of real estate registration. This method aims to ensure the secure transmission and verification of logistics information, while using the zero-knowledge proof protocol to ensure data consistency and privacy.

[0061] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0062] Figure 1 The flow chart of a method for real estate registration privacy protection based on zero-knowledge proof provided for the embodiments of the present application is as Figure 1 shown, and the method includes:

[0063] 101. Collect logistics trajectory data through multi-source sensors of the transportation carrier, encapsulate the logistics trajectory data into a physical carrier for the transfer of property rights certificates, and generate a spatio-temporal coding sequence;

[0064] In this step, the logistics trajectory data includes information such as position coordinates and speed changes collected by various sensors such as the global positioning system and accelerometers during the transportation process, and is used to record the specific path of the transportation carrier during the logistics process.

[0065] The physical carrier for the transfer of property rights certificates is an entity or digital form formed after standardizing these logistics trajectory data, and is used to prove the process of the transfer of real estate ownership.

[0066] The spatio-temporal coding sequence is a structured data format that combines timestamps and geographical coordinates, and is used to record the time and location information of each node in the logistics path in detail, ensuring that the entire logistics process is transparent and traceable.

[0067] In the embodiments of the present application, first, multi-source sensors are used to collect real-time logistics trajectory data of the transportation carrier, including but not limited to parameters such as position coordinates and speed. Then, these original data are standardized and transformed into a dataset in a unified format. Next, based on a specific algorithm, the processed data is encapsulated into a physical carrier, which not only contains the logistics trajectory data but also can reflect the key information of the transfer of rights certificates. Finally, a spatio-temporal coding sequence is generated according to each time node and the corresponding geographical location on the logistics path to ensure the transparency and verifiability of the entire logistics process.

[0068] In a specific real estate transaction case, the seller uses a transportation vehicle equipped with a global positioning system and an accelerometer to transport important documents. The system automatically records all position changes and speed changes from the starting point to the destination, forms detailed logistics trajectory data, and encapsulates it into a legally valid electronic document as the physical carrier for the transfer of rights certificates. At the same time, based on the time and location information recorded during the transportation process, a detailed spatio-temporal coding sequence is generated as valid evidence for this logistics activity.

[0069] 102. Capture dynamic voiceprint parameters based on the acoustic resonance effect in the enclosed space of the transportation carrier, synchronously encrypt the dynamic voiceprint parameters with the spatio-temporal nodes of the spatio-temporal coding sequence, and generate an encryption key that dynamically evolves along with the logistics path;

[0070] In this step, the acoustic resonance effect refers to the phenomenon of the interaction of sound waves generated by vibrations in an enclosed space, which can be used to capture dynamic voiceprint parameters, that is, the sound characteristics that change with time and space.

[0071] The dynamic voiceprint parameters are the sound characteristics obtained based on the acoustic resonance effect and are used for encryption.

[0072] The encryption key is a security code generated by synchronizing the dynamic voiceprint parameters with the spatio-temporal nodes in the spatio-temporal coding sequence, aiming to protect the data from unauthorized access, and its content will be dynamically updated as the logistics path changes.

[0073] In the embodiments of the present application, first, an acoustic wave sensor is set inside the transportation carrier to capture the dynamic voiceprint parameters generated due to the acoustic resonance effect. Then, these voiceprint parameters are analyzed to extract the main frequency components and the phase offset sequence. Next, according to the time nodes in the spatio-temporal coding sequence, the dynamic voiceprint parameters are synchronously encrypted with it to form an encryption key that continuously evolves along with the logistics path. Finally, this encryption method not only reflects the characteristics of the logistics route but also enhances the data security.

[0074] Continuing with the previous case, during transportation, the system monitored the acoustic environment inside the transportation vehicle and extracted a series of unique voiceprint features from it. After arriving at the destination, based on these voiceprint features and the timestamps on the logistics path, a unique encryption key was generated. This key is not only closely related to the logistics track but also greatly improves the security of the transmission of real estate registration documents.

[0075] 103. Bind the geographical coordinates of the spatio-temporal coding sequence to the voiceprint parameters of the encryption key, and generate a joint fingerprint of the logistics track and the voiceprint features based on the timestamp constraint conditions;

[0076] In this step, the geographical coordinates are data representing the specific location of an object on the Earth's surface, usually composed of longitude and latitude.

[0077] The voiceprint parameters of the encryption key are the voice features obtained based on the acoustic resonance effect and are used for encryption.

[0078] The joint fingerprint is a security identifier generated by binding the geographical coordinates in the spatio-temporal coding sequence to the voiceprint parameters in the encryption key and based on the timestamp constraint conditions. It is used to verify the authenticity and integrity of the logistics track and prevent any unauthorized tampering.

[0079] In the embodiment of the present application, first, the geographical coordinate information is extracted from the spatio-temporal coding sequence. Then, these geographical coordinates are combined with the voiceprint parameters in the encryption key and bound according to the timestamp constraint conditions. Then, complex algorithms and technical means are used to ensure that the created joint fingerprint can accurately reflect the logistics track and effectively prevent tampering. Finally, the generated joint fingerprint not only contains the information of the logistics path but also can verify its authenticity.

[0080] During the real estate registration process, the system constructs a joint fingerprint based on the geographical coordinates and the encryption key generated in the previous steps. This fingerprint not only proves the exact transportation path of the document, but any attempt to modify the path information will be detected due to the fingerprint mismatch, thus ensuring the security and reliability of the transaction.

[0081] 104. Split the joint fingerprint into a track shard and a voiceprint shard, perform a logical relevance verification on the track shard and the voiceprint shard through the zero-knowledge proof protocol and secure multi-party computation, recombine the track shard and the voiceprint shard only when the timestamp constraint conditions are met to verify the consistency of the real estate registration data, and isolate the complete path of the spatio-temporal coding sequence and the plaintext parameters of the encryption key.

[0082] In this step, the zero-knowledge proof protocol allows one party to verify the authenticity of the statement of another party without revealing more information.

[0083] Secure multi-party computation is a method for protecting the privacy of each party when performing computations among multiple participating parties.

[0084] Logical relevance verification is the confirmation of the relationship between trajectory shards and voiceprint shards, ensuring the consistency and integrity of data.

[0085] The timestamp constraint condition is a requirement to ensure that all operations are completed within a specific time range.

[0086] In the embodiments of this application, first, the combined fingerprint is split into trajectory shards and voiceprint shards. Then, the zero-knowledge proof protocol and secure multi-party computation technology are applied to perform logical relevance verification on these two shards. Then, only when the timestamp constraint condition is met, the shards are recombined for consistency checking. Finally, the complete path of the spatio-temporal coding sequence is isolated from the plaintext parameters of the encryption key, ensuring that only legitimate users can access the real information, thus maintaining the security and privacy of the data.

[0087] In the final verification stage of real estate registration, the registration agency receives encrypted information from the seller. By applying the zero-knowledge proof protocol and secure multi-party computation, the authenticity of the logistics trajectory is successfully verified while maintaining a high level of confidentiality of the information, ensuring the smooth completion of the transaction and protecting the privacy of all parties from infringement.

[0088] In summary, steps 101 to 104 provide a highly secure and privacy-protected real estate registration method. Through the comprehensive application of various advanced technologies such as multi-source sensor acquisition, voiceprint encryption, combined fingerprint generation, and zero-knowledge proof, not only the authenticity and integrity of the logistics trajectory data are guaranteed, but also the security of the data and the privacy protection level of users are greatly improved, ensuring that every link in the real estate transaction process is effectively monitored and protected.

[0089] To further improve the security and unique identification of the logistics trajectory and voiceprint features, the solution details how to generate the combined fingerprint of the logistics trajectory and voiceprint features according to the timestamp constraint condition. This process includes dividing geographical blocks, generating voiceprint confusion factors, aliasing geographical coordinates and voiceprint parameters, and finally generating the combined fingerprint through hash chain association, thereby enhancing data complexity and security and ensuring that the data is tamper-proof and difficult to forge. In some embodiments, generating the combined fingerprint of the logistics trajectory and voiceprint features based on the timestamp constraint condition described in step 103 includes:

[0090] 201. Divide the geographical coordinates of the spatio-temporal coding sequence into discrete geographical blocks according to the continuous timestamps of the logistics path, and each geographical block corresponds to the displacement range of the transport carrier within a fixed time interval;

[0091] In step 201, the spatio-temporal coding sequence refers to the result of digitally encoding the time and geographical coordinates of each node in the logistics process. The geographical block is an area range with a fixed time interval divided according to continuous timestamps, and each geographical block corresponds to the geographical range covered by the transport carrier within a specific time period. The spatio-temporal coding sequence contains the timestamps and corresponding geographical coordinate data of all nodes on the logistics path, and every position change in the logistics process can be accurately described through these data. The role of the geographical block is to divide the continuous logistics path into a series of discrete parts for subsequent processing.

[0092] In the embodiment of the present application, first, the timestamps and corresponding geographical coordinate data of each node on the logistics path are collected. Then, these data are segmented according to the set time interval, and all the geographical coordinate points within each period of time form a geographical block. Next, the division of the geographical block is realized through an algorithm to ensure that each geographical block represents the displacement range of the transport carrier within a fixed time interval. Finally, the discretization processing of geographical information is achieved, providing a basis for subsequent steps.

[0093] 202. Generate a voiceprint confusion factor synchronized with the geographical block based on the phase fluctuation of the voiceprint parameters of the encryption key within the corresponding time interval;

[0094] In step 202, the voiceprint parameters of the encryption key refer to the acoustic characteristic data used in the encryption process, and the phase fluctuation is the change of the phase with time during the propagation of the sound wave. The voiceprint confusion factor is a random perturbation factor based on voiceprint characteristics introduced to increase security. The voiceprint parameters contain the unique attributes of the sound signal and can be used to identify and verify identities. The phase fluctuation reflects the subtle changes that occur in the sound signal during transmission, and by using this change, a unique voiceprint confusion factor can be generated to enhance the security of the data.

[0095] In the embodiment of the present application, first, the phase fluctuation values of the voiceprint parameters within each time interval are calculated using a pre-set encryption algorithm. Then, these fluctuation values are used as the voiceprint confusion factors for this time period. Next, an additional security protection layer is added on the basis of the geographical block, so that even if the geographical location information is intercepted, the accurate information cannot be parsed without the correct voiceprint confusion factor. Finally, the security protection ability of the entire system is improved.

[0096] 203. Overlap the longitude and latitude coordinates of the geographical block with the voiceprint confusion factor, and interweave the coordinate values of the geographical block, the voiceprint confusion factor, and the corresponding timestamps based on the chaos rule to generate a block confusion unit;

[0097] In step 203, the longitude and latitude coordinates represent the specific location of the geographical block. The chaos rule is a complex mathematical model used to generate unpredictable data change patterns. The block confusion unit is a new data structure obtained through complex transformation of the geographical block coordinates, voiceprint confusion factor, and timestamp. The longitude and latitude coordinates define the spatial location of the geographical block, the chaos rule ensures a high degree of unpredictability in the data transformation process, and the block confusion unit is the product of the combination of the above elements, ensuring the security and uniqueness of the data.

[0098] In the embodiments of the present application, first, a suitable chaos system is selected to perform a non-linear transformation on the input data through its dynamic behavior. Then, techniques such as chaos mapping are used to perform parameter interleaving processing on the longitude and latitude coordinates of the geographical block, the voiceprint confusion factor, and the corresponding timestamp. Then, a block confusion unit that is not easily analyzed by reverse engineering is generated. Finally, the security of the data is enhanced, and the uniqueness of the data is ensured.

[0099] 204. According to the continuity constraint of the logistics path, the timestamp of the block confusion unit is hashed-chain associated with the voiceprint confusion factor to generate a joint fingerprint of the logistics trajectory and the voiceprint feature.

[0100] In step 204, the hashed-chain association refers to a method of using a hash function to link data with different timestamps in a chain form to ensure the integrity and order of the data. The joint fingerprint is the final unique identifier that combines the logistics trajectory and the voiceprint feature. The hashed-chain association not only ensures the integrity of the data but also maintains the logical order between the data, and the joint fingerprint provides a unique verification basis.

[0101] In the embodiments of the present application, first, according to the continuity principle of the logistics path, hash operations are sequentially performed on the timestamps and voiceprint confusion factors of each block confusion unit. Then, they are linked in chronological order to form an immutable link. Then, a joint fingerprint containing the logistics trajectory and the voiceprint feature is generated in this way. Finally, the one-way and collision-resistant properties of the hash function are utilized to ensure the security and reliability of the joint fingerprint.

[0102] The following is a specific example:

[0103] In a real estate transaction case, the seller hopes to ensure the authenticity and security of logistics documents. First, the seller collects and encodes the timestamps and geographical coordinates of each key node in the logistics process. Then, for each period of time, a unique voiceprint confusion factor is calculated to enhance data confidentiality. Subsequently, chaotic rules are used to perform aliasing processing on the geographical coordinates, voiceprint confusion factor, and timestamps to create secure block confusion units. Finally, through the hash chain association method, all block confusion units are connected into a string to form a unique combined fingerprint, ensuring the authenticity and integrity of the logistics documents.

[0104] In summary, steps 201 to 204 construct an efficient and secure combined fingerprint mechanism by combining timestamps, geographical coordinates, voiceprint features, and chaos theory. This not only improves the anti-counterfeiting ability of the logistics track but also enhances the security during data transmission, effectively preventing the risk of data being illegally tampered with or forged. At the same time, this method is also applicable to other fields that require high security guarantees, such as financial transactions and the transmission of important documents.

[0105] To solve the problem of geographical block information confusion and protection, the solution uses chaotic rules for parameter interweaving to generate block confusion units. This step increases the complexity of data processing by introducing dynamic perturbation and cross-over superposition techniques, effectively enhancing the data security protection level. In some embodiments, the aliasing of the longitude and latitude coordinates of the geographical block and the voiceprint confusion factor in step 203, and the parameter interweaving of the coordinate values of the geographical block, the voiceprint confusion factor, and the corresponding timestamp based on chaotic rules to generate block confusion units includes:

[0106] 301. Decompose the longitude and latitude coordinates of the geographical block into a longitude sequence and a latitude sequence, and generate a coordinate permutation sequence based on the closed-space parameters of the transportation carrier.

[0107] In step 301, the longitude and latitude coordinates of the geographical block are understood as a data pair describing a specific geographical location, consisting of longitude and latitude. The closed-space parameters of the transportation carrier refer to a series of values set according to the internal environmental characteristics of the transportation tool, used to adjust the way of coordinate permutation. The coordinate permutation sequence is a new coordinate sequence obtained through the above parameter transformation, used to increase the security of the coordinate data.

[0108] In the embodiments of the present application, first, the longitude and latitude coordinates of the geographical block are decomposed into two independent sequences, namely the longitude sequence and the latitude sequence. Then, an algorithm model is generated based on the closed-space parameters of the transportation carrier. This model can rearrange the coordinates according to preset rules to generate a coordinate permutation sequence. The final result is a coordinate sequence that has been processed and is difficult to directly identify the original location information.

[0109] 302. Extract the frequency-domain features and time-domain periods of the voiceprint confusion factor according to the displacement range of the logistics path corresponding to the time stamp of the time window associated with the geographical block, and generate an interleaving weight vector.

[0110] In step 302, the time window refers to the time range associated with the geographical block, and the time stamp is the data identifying a specific time point. The displacement range of the logistics path represents the distance interval that an item moves during the logistics process. The frequency-domain features and time-domain periods of the voiceprint confusion factor are respectively the characteristic manifestations of the sound signal in the frequency dimension and the time dimension. The interleaving weight vector is generated based on these features and is a set of numerical values used to adjust the interleaving degree between different data.

[0111] In the embodiment of the present application, first, determine the time window corresponding to the geographical block and extract the displacement range of the logistics path within this time period. Subsequently, analyze the frequency-domain features and time-domain periods of the voiceprint confusion factor, and use a specific algorithm to calculate the interleaving weight vector. This process utilizes signal processing technology to ensure that the generated interleaving weight vector can effectively reflect the complex relationship between data, and finally generates the interleaving weight vector for use in subsequent steps.

[0112] 303. Perform bit-order offset on the longitude sequence, latitude sequence, and the interleaving weight vector, and perform iterative permutation on the offset coordinate components and the voiceprint confusion factor based on the chaotic initial perturbation value to generate a coordinate-voiceprint interleaving vector.

[0113] In step 303, the longitude sequence and latitude sequence refer to two independent data sequences formed after decomposing the longitude and latitude coordinates of the geographical block. The interleaving weight vector is generated based on the voiceprint confusion factor in the previous step and is a set of numerical values used to adjust the interleaving degree between different data. Bit-order offset refers to the process of adjusting the positions of elements in the sequence. The chaotic initial perturbation value is a random number generated based on chaos theory and is used to increase the complexity and security of the encryption process. The coordinate component refers to the specific numerical value of longitude or latitude. The coordinate-voiceprint interleaving vector is a new vector that has undergone iterative permutation processing and integrates geographical location information and voiceprint features.

[0114] In the embodiment of the present application, first, perform bit-order offset processing on the longitude sequence and latitude sequence obtained in step 301 and the interleaving weight vector generated in step 302, and change the position relationship of the internal elements of these sequences through a specific algorithm to initially disrupt the original structure. Then, use the chaotic initial perturbation value as an input parameter to perform iterative permutation operations on the offset coordinate components (i.e., longitude and latitude values) and the voiceprint confusion factor. In this process, an algorithm based on chaotic mapping is adopted to ensure that each iteration can produce highly random and unpredictable results. Finally, integrate the above processing results to form a coordinate-voiceprint interleaving vector, realizing the effective fusion and protection of geographical information and voiceprint features.

[0115] 304. Dynamically perturb the coordinate voiceprint interleaved vector according to the moving direction of the transport carrier, and cross-superimpose the perturbed coordinate voiceprint interleaved vector with the coordinate permutation sequence to generate a block confusion unit.

[0116] In step 304, the direction of movement of the transport carrier refers to the direction of movement of the carrier (such as a vehicle, ship, etc.) during logistics or transportation. Dynamic perturbation refers to the process of real-time adjustment of data according to specific rules to increase security and unpredictability. The coordinate voiceprint interleaved vector is a new vector generated in the previous step that combines geographic location information and voiceprint features. The coordinate permutation sequence is a sequence of rearranged coordinates generated based on the closed space parameters of the transport carrier in step 301. The block confusion unit is ultimately an encrypted data packet formed by cross-superposition of the coordinate voiceprint interleaved vector and the coordinate permutation sequence after dynamic perturbation processing.

[0117] In an embodiment of the present application, first, an algorithm model is used to apply dynamic disturbance to the coordinate voiceprint interleaved vector according to the actual direction of movement of the transport carrier. This process involves using sensors or other means to obtain the current direction of movement of the carrier, and fine-tuning the data based on this direction. Next, the coordinate voiceprint interleaved vector after dynamic disturbance is cross-superimposed with the coordinate permutation sequence generated in step 301. The cross-superposition here can be understood as combining the elements in the two sequences according to a certain rule to form a new sequence. This process may involve complex mathematical or logical operations, aimed at further confusing the original data and increasing the difficulty of cracking. The end result is a block confusion unit that is difficult to directly associate back to the original geographic block location information.

[0118] Here is a specific example:

[0119] In a specific real estate transaction case, in order to maximize the protection of the privacy of its geographic information, the seller proceeds according to the method of step 304 after completing the previous steps. First, the real-time movement direction of the transport carrier is obtained through the global positioning device installed on the transport vehicle, and then the coordinate voiceprint interleaving vector obtained in step 303 is dynamically adjusted according to this direction. Next, these dynamically disturbed data are combined with the coordinate permutation sequence generated in step 301, and the cross-overlapping of the two is achieved through a specific algorithm. The result of this is the generation of a highly encrypted block obfuscation unit, which ensures that even if it is intercepted during the data transmission process, it is extremely difficult to restore the seller's specific location information.

[0120] In summary, steps 301 to 304 not only effectively hide the original location information by performing multi-level transformation and encryption on the geographical block information, but also enhance the security during the data transmission process. The entire process combines modern cryptography principles and signal processing techniques, providing strong technical support for various application scenarios that require confidentiality. In particular, step 303 increases the cracking difficulty by introducing chaos theory, further improving the robustness and anti-attack ability of the system.

[0121] To further address the problem of insufficient data security during the encryption of geographical block information, this solution deeply explores the process of iterative permutation of coordinate components and voiceprint confusion factors based on the chaotic initial perturbation value, emphasizing the generation of dynamic chaotic seeds using parameters such as the resonance frequency of the enclosed space of the transportation carrier and real-time acceleration, and realizing the generation of the coordinate-voiceprint interleaved vector through cyclic displacement and parameter replacement, strengthening the dynamic evolution characteristics of the encryption key. In some embodiments, the iterative permutation of the offset coordinate components and voiceprint confusion factors in step 304 to generate the coordinate-voiceprint interleaved vector includes:

[0122] 401. Generate a dynamic chaotic seed associated with the chaotic initial perturbation value based on the resonance frequency of the enclosed space of the transportation carrier and the displacement range of the logistics path;

[0123] In step 401, the resonance frequency of the enclosed space of the transportation carrier refers to an inherent vibration frequency determined according to the internal structural characteristics of the transportation tool. The displacement range of the logistics path represents the distance interval that the item moves during the entire logistics process. The dynamic chaotic seed is a random number sequence generated based on the above two parameters and the chaotic initial perturbation value, and is used for subsequent data encryption processes.

[0124] In the embodiments of the present application, first, the resonance frequency of the enclosed space of the transportation carrier is measured, and the corresponding values are calculated in combination with the displacement range of the logistics path. Then, these values and the chaotic initial perturbation value are used to generate a dynamic chaotic seed through a specific algorithm. This process adopts a method combining signal processing technology and chaos theory to ensure that the generated dynamic chaotic seed has high randomness and unpredictability. Finally, a dynamic chaotic seed that can effectively enhance data security is obtained.

[0125] 402. Arrange the offset coordinate components in the order of time stamps as an input vector, and inject the dynamic chaotic seed into the head and tail nodes of the input vector to generate a chaotic iterative input queue;

[0126] In step 402, the offset coordinate component refers to the specific value of the longitude or latitude after the bit order adjustment. The timestamp sequence arrangement means sorting the data in the order of the time sequence of the event occurrence. The chaotic iteration input queue is a new data sequence formed by injecting the dynamic chaotic seed into the head and tail nodes of the input vector sorted by the timestamp.

[0127] In the embodiment of the present application, first, the offset coordinate components are sorted according to the corresponding timestamps to form an input vector. Then, the dynamic chaotic seeds generated in the above steps are respectively added to the start and end positions of the input vector to construct a chaotic iteration input queue. Here, simple list operations are used to realize the reorganization of the data, ensuring the security and integrity of the data. Finally, a chaotic iteration input queue containing the original data and additional encryption elements is formed.

[0128] 403. Obtain the iteration step length of the chaotic mapping according to the real-time acceleration and vibration intensity of the transport carrier, and perform cyclic displacement and parameter replacement on the vector elements of the chaotic iteration input queue to generate a chaotic perturbation intermediate vector;

[0129] In step 403, the real-time acceleration and vibration intensity of the transport carrier reflect the dynamic change situation during the vehicle driving process. The iteration step length of the chaotic mapping is a value calculated according to these dynamic parameters, which is used to guide the generation of the chaotic perturbation intermediate vector. The chaotic perturbation intermediate vector is a new vector after the cyclic displacement and parameter replacement of the chaotic iteration input queue.

[0130] In the embodiment of the present application, first, the real-time acceleration and vibration intensity of the transport carrier are monitored, and the iteration step length of the chaotic mapping is determined by analyzing these data. Then, this step length is used to perform cyclic displacement and parameter replacement operations on the chaotic iteration input queue. Specifically, complex mathematical operations are implemented to change the arrangement of the original data, increasing the cracking difficulty. Finally, a chaotic perturbation intermediate vector that contains both the original geographical information and the dynamic perturbation characteristics is generated.

[0131] 404. Segment and intercept the chaotic perturbation intermediate vector according to the logistics path corresponding to the timestamp, and perform cross-reorganization on the longitude sequence, latitude sequence, and voiceprint confusion factor to generate a coordinate voiceprint interleaved vector.

[0132] In step 404, the chaotic perturbation intermediate vector refers to the data sequence after being processed in the previous step, which contains the offset coordinate components and the influence of the dynamic chaotic seed. The logistics path segment corresponding to the time stamp refers to the different path segments divided according to the movement of the item at different time periods during the logistics process. Cross recombination is a process of recombining the longitude sequence, latitude sequence, and voiceprint confusion factor according to certain rules, aiming to generate an encrypted data structure and a coordinate voiceprint interleaved vector that contains both geographical location information and integrates voiceprint features.

[0133] In the embodiment of the present application, first, identify and intercept the part of the chaotic perturbation intermediate vector related to a specific time stamp, and this part corresponds to a certain segment in the logistics path. Then, for each segment, cross recombine the longitude sequence, latitude sequence, and voiceprint confusion factor within the segment according to a preset algorithm. Here, methods based on genetic algorithms or neural networks can be adopted, and effective fusion between different data is achieved by adjusting parameter weights. Finally, by processing the entire chaotic perturbation intermediate vector in this way, a complex and difficult-to-inverse-analyze coordinate voiceprint interleaved vector is generated to ensure the security of geographical information and voiceprint features.

[0134] The following is a specific example:

[0135] In a specific real estate transaction case, the seller hopes to further enhance the security protection measures for its location information. After completing the previous steps, continue according to the methods of steps 401 to 404. First, generate a dynamic chaotic seed based on the resonance frequency of the enclosed space of the transportation carrier and the displacement range of the logistics path. Then, add the dynamic chaotic seed to form a chaotic iteration input queue. Then, determine the iteration step size of the chaotic mapping according to the real-time acceleration and vibration intensity of the transportation carrier, and process the chaotic iteration input queue to generate a chaotic perturbation intermediate vector. Finally, intercept the chaotic perturbation intermediate vector according to the logistics path segment to generate a coordinate voiceprint interleaved vector. For example, during a specific time period, the logistics path shows that there are specific transportation activities in the area where the real estate is located, then the data for this time period is specially processed to add an extra security layer, so that it is difficult to restore the complete geographical location information even if some data segments are obtained.

[0136] In summary, steps 401 to 404, through a series of complex data transformation and encryption means, not only effectively hide the location information of geographical blocks, but also greatly improve the security during the data transmission process. The entire process is designed rigorously, combining modern cryptography principles, signal processing technologies, and the application of physical parameters, providing strong technical support for various application scenarios that require confidentiality. Especially for fields such as real estate transactions involving sensitive information, it significantly improves the data protection ability and privacy protection level.

[0137] To solve the problem of verifying the logical relevance between geographical block information and voiceprint data, the solution introduces a specific method for performing logical relevance verification on trajectory sharding and voiceprint sharding through the zero-knowledge proof protocol and secure multi-party computation. The key lies in generating challenge vectors based on the speed change and resonance frequency parameters, as well as generating response rules based on real-time state parameter synchronization, to ensure data consistency and privacy protection while improving verification efficiency. In some embodiments, the logical relevance verification of the trajectory sharding and voiceprint sharding through the zero-knowledge proof protocol and secure multi-party computation described in step 104 includes:

[0138] 501. Apply the speed change parameters of the transportation carrier and the resonance frequency parameters of the enclosed space to the trajectory sharding and voiceprint sharding respectively to generate a trajectory challenge vector and a voiceprint challenge vector;

[0139] In step 501, the speed change parameters of the transportation carrier refer to a series of values determined according to the speed change of the transportation tool during driving. The resonance frequency parameters of the enclosed space are the natural vibration frequencies calculated based on the internal structural characteristics of the transportation tool. The trajectory challenge vector and the voiceprint challenge vector are data sequences generated by applying the above parameters to the trajectory sharding and voiceprint sharding respectively, and are used for subsequent security verification processes.

[0140] In the embodiments of the present application, first, the speed change parameters of the transportation carrier and the resonance frequency parameters of the enclosed space are collected, and then these parameters are used to process the trajectory sharding and voiceprint sharding to generate corresponding challenge vectors. Here, signal processing techniques are combined with specific algorithms to adjust the original data to ensure that the generated challenge vectors can reflect the influence of the physical environment on the data. The final result is two new data sequences, namely the trajectory challenge vector and the voiceprint challenge vector, which not only contain the original information but also add an additional security layer.

[0141] 502. Split the trajectory challenge vector and the voiceprint challenge vector into verification shard groups according to the constraint conditions of the time stamp, and distribute the confused trajectory sharding and voiceprint sharding to different registration agencies;

[0142] In step 502, the constraint conditions of the time stamp refer to the standard for sorting data in the order of the time of event occurrence. The verification shard group is a data set obtained by splitting and reorganizing the trajectory challenge vector and the voiceprint challenge vector according to the time stamp. The confused trajectory sharding and voiceprint sharding refer to encrypted data segments to prevent unauthorized access.

[0143] In the embodiments of the present application, first, the trajectory challenge vector and the voiceprint challenge vector are split into multiple small segments according to the constraint conditions of timestamps to form a verification shard group. Then, these shards are obfuscated through a security protocol, and the obfuscated shards are distributed to different registration institutions. This process involves complex mathematical operations and data encryption technologies, ensuring the security of data transmission. Finally, a distributed data structure that is difficult to reverse-engineer is formed, improving the security of the overall system.

[0144] 503. Generate a response generation rule based on the real-time status parameters of the transportation carrier, and synchronize the response generation rule among the registration institutions through secure multi-party computation;

[0145] In step 503, the real-time status parameters of the transportation carrier include dynamic information such as speed and direction. The response generation rule is a guiding principle formulated based on these status parameters and is used to synchronize the response mechanisms among different registration institutions. Secure multi-party computation is a technology that allows multiple parties to jointly execute a computing task without revealing their respective data privacy.

[0146] In the embodiments of the present application, first, the real-time status parameters of the transportation carrier are monitored, and based on this, a response generation rule is generated. Then, through secure multi-party computation technology, these rules are synchronized among different registration institutions. This step utilizes advanced cryptographic algorithms and distributed computing frameworks to ensure that each institution can cooperate without revealing sensitive information. Finally, the ability of all participating parties to process data according to a unified standard is achieved.

[0147] 504. In the zero-knowledge proof protocol, perform segmented perturbation on the verification shard group according to the response generation rule to obtain local response shards, and the local response shards contain timestamp continuity identifiers;

[0148] In step 504, the zero-knowledge proof protocol is a technology that allows one party (the verifier) to verify the claim of another party (the prover) without obtaining any useful information. The response generation rule is a set of algorithms or logics formulated based on the real-time status parameters of the transportation carrier and is used to guide how to process data to generate a valid response. Segmented perturbation refers to the process of locally adjusting and encrypting data, aiming to protect data privacy while ensuring its consistency. The local response shards are data segments generated after the above process and contain timestamp continuity identifiers for subsequent verification.

[0149] In the embodiments of the present application, first, a specific processing flow is defined according to the response generation rule. Then, under the framework of the zero-knowledge proof protocol, a segmented perturbation operation is performed on each verification shard group. Here, specific encryption algorithms and logical operations are used to adjust the data to ensure that even if part of the data is intercepted, the complete information cannot be easily parsed. The data processed in this way forms multiple local response shards, and each shard contains a timestamp continuity identifier for verifying the consistency of the time order of the data. The final result is a data structure that protects the privacy of the original data and can verify the consistency.

[0150] 505. Cross-verify the local response shards. Only when the identifier meets the timestamp continuity and is consistent with the response generation rule, trigger shard recombination to verify the consistency of the real estate registration data.

[0151] In step 505, cross-verification refers to the process of comparing data from different sources with each other to confirm their consistency and authenticity. The timestamp continuity identifier is a mark added to the local response shards in the previous step, used to identify whether the time series corresponding to the shard is continuous. Shard recombination is to merge the verified local response shards to restore the complete data set and verify the consistency of the real estate registration data.

[0152] In the embodiments of the present application, first, cross-verification is performed on all local response shards to check whether the timestamp continuity identifiers in each shard meet the expectations and compare them with the response generation rule. If all identifiers meet the continuity requirements and are consistent with the rule, trigger the shard recombination process. In this process, a specific algorithm is used to recombine the scattered data into a whole. This process not only verifies the consistency of the time order of the data but also ensures the authenticity and integrity of the data. Finally, a set of strictly verified real estate registration data is obtained, ensuring the security and reliability of the entire transaction process.

[0153] The following is a specific example:

[0154] In a specific real estate transaction case, the seller hopes to ensure the consistency and security between the geographical location information provided and the voiceprint data. After completing the previous steps, continue the operation according to the methods in steps 501 to 505. First, generate a trajectory challenge vector and a voiceprint challenge vector based on the speed change of the transportation carrier and the resonance frequency of the enclosed space. Then, split these vectors according to the time stamps and distribute the scrambled data to different registration agencies. Next, formulate a response generation rule based on the real-time state parameters of the transportation carrier and synchronize these rules through secure multi-party computation. Subsequently, perform segmented perturbation on the verification shard group under the zero-knowledge proof protocol to obtain local response shards containing time stamp continuity identifiers. Finally, when the identifier meets the time stamp continuity and is consistent with the response generation rule, trigger shard recombination to verify the consistency of the real estate registration data. The result of this is to ensure a high level of security and data consistency throughout the transaction process.

[0155] In summary, steps 501 to 505, by comprehensively applying various advanced technical means such as the zero-knowledge proof protocol and secure multi-party computation, not only effectively verify the logical relevance between the geographical block information and the voiceprint data, but also significantly enhance the data security and consistency in the real estate registration process. The entire process is rigorously designed, fully considering various complex factors in the actual application scenario, providing strong technical support for fields such as real estate transactions involving sensitive information. Especially while protecting user privacy, it ensures the authenticity and integrity of the data.

[0156] In order to further improve the data security and privacy protection effect in the real estate registration process, this solution focuses on elaborating the process of generating a response generation rule based on the real-time state parameters of the transportation carrier. By analyzing the amplitude fluctuation of the dynamic state sequence and the frequency domain offset of the voiceprint scrambling factor, combined with the curvature radius of the logistics path, a dynamic weight factor is generated and intertwined with the trajectory and voiceprint shards to form a rule template, enhancing the adaptability and flexibility of the rule template. In some embodiments, the generating a response generation rule based on the real-time state parameters of the transportation carrier described in step 503 includes:

[0157] 601. Divide the real-time speed, acceleration, and vibration intensity of the enclosed space of the transportation carrier into a dynamic state sequence according to the time stamp, and extract the state parameter components corresponding to each time stamp;

[0158] In step 601, the real-time speed, acceleration, and vibration intensity of the transportation carrier are a dataset describing the dynamic behavior of the transportation tool. The time stamp is used to identify the time point of each data record. The dynamic state sequence is a data sequence formed by arranging these parameters in chronological order, used to reflect the state changes of the transportation carrier at different time points. The state parameter component refers to the specific value extracted from the dynamic state sequence.

[0159] In the embodiments of the present application, first, the real-time speed, acceleration, and vibration intensity of the enclosed space of the transportation carrier are collected, and these parameters are sorted according to the time stamp to form a dynamic state sequence. Then, the state parameter components corresponding to each time stamp are extracted from this sequence as the basis for subsequent processing. Here, sensor technology is used to obtain the original data, and a simple sorting algorithm is used to organize it into a sequence form. Finally, a data set that can accurately reflect the dynamic characteristics of the transportation carrier is obtained.

[0160] 602. Generate a dynamic weight factor bound to the time stamp based on the amplitude fluctuation of the dynamic state sequence and the frequency-domain offset of the voiceprint confusion factor, where the generation of the dynamic weight factor is constrained by the curvature radius of the logistics path;

[0161] In step 602, the dynamic weight factor is a numerical value generated based on the amplitude fluctuation of the dynamic state sequence and the frequency-domain offset of the voiceprint confusion factor, and is used to adjust the weights of the data processing rules corresponding to different time stamps. The curvature radius of the logistics path is a key geometric parameter describing the degree of curvature of the logistics path, and is used as a constraint condition to ensure the rationality and applicability of the dynamic weight factor. The amplitude fluctuation refers to the degree of numerical change in the dynamic state sequence, and the frequency-domain offset is the position movement of the voiceprint confusion factor in the frequency domain.

[0162] In the embodiments of the present application, first, the amplitude fluctuation of the dynamic state sequence and the frequency-domain offset of the voiceprint confusion factor are calculated. Then, according to the curvature radius of the logistics path, a constraint condition is set, and a specific algorithm is used to generate a dynamic weight factor bound to the time stamp. This process uses signal processing technology and mathematical models to ensure that the dynamic weight factor can not only reflect the actual state of the transportation carrier but also meet the requirements of the path characteristics. Finally, a set of dynamic weight factors is generated, providing a basis for the construction of subsequent rule templates.

[0163] 603. Interleave the parameters of the dynamic weight factor with the trajectory slices and voiceprint slices corresponding to the time stamp to generate a rule template including time stamp constraint conditions;

[0164] In step 603, the rule template is a data structure including time stamp constraint conditions, which is used to guide the formulation of subsequent response generation rules. Parameter interleaving refers to the process of combining the dynamic weight factor with the trajectory slices and voiceprint slices, aiming to enhance the security and consistency of the data. The time stamp constraint condition is a standard for sorting data according to the time sequence of event occurrence, ensuring the time continuity and logical relationship of the data.

[0165] In the embodiments of the present application, first, a parameter interleaving operation is performed on the dynamic weight factor and the trajectory shards and voiceprint shards corresponding to the time stamps, and complex mathematical operations and logical operations are used to achieve effective fusion of the data. Then, a rule template including time stamp constraint conditions is generated according to the interleaving result. Here, a variety of encryption algorithms are involved to ensure that the rule template not only has high security, but also can ensure the time order consistency and logical relevance of the data. Finally, a rule template that contains both time information and high security is formed, providing guidance for subsequent data processing.

[0166] 604. Shard-encrypt the rule template through secure multi-party computation, distribute the encrypted rule shards to the registration agencies, and generate a response generation rule based on multi-party collaborative decryption.

[0167] In step 604, shard-encryption is a process of splitting and encrypting the rule template, aiming to protect data privacy. The encrypted rule shards are fragments of the rule template after encryption processing, ensuring that even if part of the data is intercepted, the complete information cannot be easily parsed. Multi-party collaborative decryption is a technology that allows multiple participating parties to jointly complete the decryption task without revealing their respective data. Through this mechanism, efficient information sharing and verification can be achieved, while ensuring the security and privacy of the data.

[0168] In the embodiments of the present application, first, the rule template is shard-encrypted, and advanced encryption algorithms are used to ensure the security of each fragment. Then, the encrypted rule shards are distributed to different registration agencies. On this basis, through secure multi-party computation technology, each agency collaborates to complete the decryption task, and finally a response generation rule is generated. The whole process not only ensures the security of data transmission, but also realizes an efficient information sharing and verification mechanism. Finally, a complete set of response generation rules is obtained to guide subsequent data processing and verification work.

[0169] The following is a specific example:

[0170] In a specific real estate transaction case, the seller hopes to ensure the consistency and security between the geographical location information and the voiceprint data provided by it. After completing the previous steps, continue to operate according to the methods of steps 601 to 604. First, collect the real-time speed, acceleration, and vibration intensity of the enclosed space of the transportation carrier to form a dynamic state sequence. Then, generate a dynamic weight factor based on the dynamic state sequence and the voiceprint confusion factor, and generate a rule template. Then, shard-encrypt the rule template and distribute the encrypted rule shards to different registration agencies to generate a response generation rule. This not only enhances the security and consistency of the data, but also ensures the transparency and credibility of the entire transaction process.

[0171] In summary, steps 601 to 604, by comprehensively applying various advanced technical means such as sensor technology, signal processing technology, mathematical modeling, and encryption algorithms, not only effectively solve the consistency problem between the real-time state parameters of the transport carrier and the data processing rules, but also significantly improve the data security and privacy protection level in the real estate registration process. The entire process is designed rigorously, fully considering various complex factors in the actual application scenario, providing strong technical support for fields such as real estate transactions involving sensitive information, and greatly enhancing information security and user trust. Especially while protecting data privacy, the authenticity and integrity of the data are ensured. In addition, through the multi-party collaborative decryption mechanism, the security and reliability of the data are further strengthened, ensuring transparency and fairness in the information sharing process.

[0172] To further improve the security and privacy protection effect of data transmission in the logistics path, this solution details a method for synchronously encrypting dynamic voiceprint parameters and spatio-temporal coding sequences to generate an encryption key, including analyzing phase fluctuations to extract the phase offset sequence corresponding to the main frequency component, interleaving the spatio-temporal coding sequence to generate a mixed coding block, and performing modulo addition operations through a chaotic perturbation vector generated by a hash chain, ultimately generating an encryption key that dynamically evolves along with the logistics path, improving the security and dynamic adaptability of the key. In some embodiments, the step of synchronously encrypting the dynamic voiceprint parameters with the spatio-temporal nodes of the spatio-temporal coding sequence in step 102 to generate an encryption key that dynamically evolves along with the logistics path includes:

[0173] 701. Perform phase fluctuation analysis on the dynamic voiceprint parameters, extract the phase offset sequence corresponding to its main frequency component, and quantize the integer sequence with a time stamp bit width matching the spatio-temporal nodes;

[0174] In step 701, the dynamic voiceprint parameters refer to voiceprint feature data that changes with time and environment. Phase fluctuation analysis is a process of processing these data to extract their phase change characteristics. The main frequency component is the most dominant frequency component in the voiceprint signal, and the phase offset sequence is the phase change value extracted from the main frequency component. Quantizing the integer sequence with a time stamp bit width matching the spatio-temporal nodes means converting the time stamp into an integer sequence that matches the spatio-temporal nodes for subsequent operations.

[0175] In the embodiments of this application, first, phase fluctuation analysis is performed on the dynamic voiceprint parameters to identify and extract the main frequency component and its corresponding phase offset sequence. Then, an integer sequence is generated according to the time stamp of the spatio-temporal nodes, ensuring that the bit width of this sequence matches the spatio-temporal nodes. Digital signal processing technology and specific algorithms are used here to achieve data conversion and quantization. The final result is a phase offset sequence that can accurately reflect the changes in voiceprint characteristics and its corresponding time stamp integer sequence.

[0176] 702. Interleave the longitude and latitude coordinates of the spatio-temporal coding sequence with the timestamp to generate a mixed coding block, and alternately flip the bits of the mixed coding block based on the parity of the phase offset sequence to generate an initial key segment;

[0177] In step 702, the spatio-temporal coding sequence is a data set composed of longitude and latitude coordinates and timestamps for identifying specific geographical locations and times. The mixed coding block is a data structure generated by interleaving the longitude and latitude coordinates in the spatio-temporal coding sequence with the timestamp. The initial key segment is a data fragment obtained after performing a bit flipping operation on the mixed coding block, and the flipping method is determined based on the parity of the phase offset sequence.

[0178] In the embodiment of the present application, first, the longitude and latitude coordinates in the spatio-temporal coding sequence are interleaved with the timestamp to generate a mixed coding block. Then, logical operation and mathematical transformation techniques are adopted to alternately flip the bits of the mixed coding block according to the parity of the phase offset sequence to generate an initial key segment, ensuring that the generated initial key segment contains both the original spatio-temporal information and certain security. Finally, a preliminary key segment is formed, providing a basis for subsequent encryption.

[0179] 703. Generate a chaotic perturbation vector based on the hash chain of the continuous timestamps of the logistics path, and perform a bitwise modulo addition operation on the initial key segment and the chaotic perturbation vector to generate an intermediate key segment with the dynamic characteristics of the logistics path;

[0180] In step 703, the hash chain is a series of continuous values generated based on a hash function, used to represent the chronological relationship of the logistics path. The chaotic perturbation vector is a random number sequence generated based on the hash chain, used to increase the complexity of the encryption process. The intermediate key segment is a data fragment generated by performing a modulo addition operation on the initial key segment and the chaotic perturbation vector, and has the dynamic characteristics of the logistics path.

[0181] In the embodiment of the present application, first, a hash chain is generated based on the continuous timestamps of the logistics path, and the chaotic perturbation vector is generated using this hash chain. Then, a bitwise modulo addition operation is performed on the initial key segment and the chaotic perturbation vector. Here, a method combining cryptography technology and chaos theory is adopted to generate an intermediate key segment with the dynamic characteristics of the logistics path, ensuring that the generated intermediate key segment is difficult to be predicted or cracked. Finally, an intermediate key segment that contains both the original spatio-temporal information and high security is obtained.

[0182] 704. Perform multiple rounds of displacement perturbations on the intermediate key segment according to the main frequency energy decay curve of the voiceprint to generate an encryption key that dynamically evolves with the logistics path.

[0183] In step 704, the main frequency energy attenuation curve of the voiceprint is a curve that describes the trend of the energy of the voiceprint signal changing with time on its main frequency components. Multiple-round displacement perturbation refers to the process of performing multiple position movements and transformations on the data based on specific rules to increase the security and unpredictability of the encryption key. The intermediate key segment is the preliminary encryption key data generated from the previous step, which will form the final encryption key after further processing. This key has the characteristic of dynamically evolving along with the logistics path.

[0184] In the embodiments of the present application, first, the main frequency energy attenuation curve in the dynamic voiceprint parameters is analyzed, and a multiple-round displacement perturbation scheme is designed according to its characteristics. Then, multiple position movement and transformation operations are performed on the intermediate key segment according to the predetermined scheme. Complex mathematical operations and logical operations are used here to adjust the data structure to ensure that each round of displacement perturbation can significantly change the key content and increase the cracking difficulty. For example, methods such as cyclic displacement and permutation can be adopted, and different perturbation strategies are applied round by round in combination with the characteristics of different stages of the main frequency energy attenuation curve of the voiceprint. The final result is an encryption key that not only contains the original spatio-temporal information but also has high security and can be dynamically updated along with the change of the logistics path.

[0185] The following is a specific example:

[0186] In a specific real estate transaction case, the seller hopes to ensure the security of the geographical location information provided by it. After completing the previous steps, continue to operate according to the methods of steps 701 to 704. First, perform phase fluctuation analysis on the dynamic voiceprint parameters and quantify the integer sequence of the spatio-temporal node timestamp bit-width matching. Then, interweave the longitude and latitude coordinates of the spatio-temporal coding sequence with the timestamp to generate a mixed coding block and generate the initial key segment. Then, perform modulo addition operation on the initial key segment and the chaotic perturbation vector to generate an intermediate key segment with the dynamic characteristics of the logistics path. Finally, generate an encryption key that dynamically evolves along with the logistics path. The result of doing so is to ensure a high degree of confidentiality and security of the geographical information throughout the transaction process.

[0187] In summary, steps 701 to 704, by combining dynamic voiceprint parameters, spatio-temporal coding sequences, and logistics path characteristics, and adopting advanced digital signal processing, cryptography, and chaos theory technologies, not only effectively solve the synchronous encryption problem of dynamic voiceprint parameters and spatio-temporal coding sequences, but also significantly improve the security and privacy protection level of data transmission in the logistics path. The entire process is designed rigorously, fully considering various complex factors in the actual application scenario, providing strong technical support for fields such as real estate transactions involving sensitive information, greatly enhancing information security and user trust. Especially while protecting data privacy, it ensures the authenticity and integrity of the data. In addition, by introducing the dynamic characteristics of the logistics path, the security and unpredictability of the encryption key are further enhanced.

[0188] Figure 2 The structural schematic diagram of a real estate registration privacy protection system based on zero - knowledge proof is provided for the embodiments of this application. As Figure 2 shown, the device includes:

[0189] A collection module 21 that collects logistics track data through multi - source sensors of a transportation carrier, encapsulates the logistics track data into a physical carrier for warrant transfer, and generates a spatio - temporal coding sequence;

[0190] An encryption module 22 that captures dynamic voiceprint parameters based on the acoustic resonance effect in the enclosed space of the transportation carrier, synchronously encrypts the dynamic voiceprint parameters with the spatio - temporal nodes of the spatio - temporal coding sequence, and generates an encryption key that dynamically evolves along the logistics path;

[0191] A generation module 23 that binds the geographical coordinates of the spatio - temporal coding sequence with the voiceprint parameters of the encryption key, and generates a joint fingerprint of the logistics track and voiceprint features based on timestamp constraint conditions;

[0192] A verification module 24 that splits the joint fingerprint into track shards and voiceprint shards, performs logical relevance verification on the track shards and voiceprint shards through a zero - knowledge proof protocol and secure multi - party computation, recombines the track shards and voiceprint shards only when the timestamp constraint conditions are met to verify the consistency of real estate registration data, and isolates the complete path of the spatio - temporal coding sequence and the plaintext parameters of the encryption key.

[0193] Figure 2 The real estate registration privacy protection system based on zero - knowledge proof can execute Figure 1 the real estate registration privacy protection method based on zero - knowledge proof described in the embodiments shown. Its implementation principle and technical effects will not be elaborated further. For the real estate registration privacy protection system based on zero - knowledge proof in the above - mentioned embodiments, the specific ways for each module and unit to perform operations have been described in detail in the embodiments related to this method, and will not be elaborated here.

[0194] In a possible design, Figure 2 the real estate registration privacy protection system based on zero - knowledge proof in the embodiments shown can be implemented as a computing device. As Figure 3 shown, the computing device can include a storage component 31 and a processing component 32;

[0195] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32.

[0196] The processing component 32 is used for the above - mentioned Figure 1A real estate registration privacy protection method based on zero - knowledge proof in the described embodiment.

[0197] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above - mentioned method. Of course, the processing component can also be implemented by one or more application - specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field - programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above - mentioned method.

[0198] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non - volatile storage device or a combination thereof, such as static random - access memory (SRAM), electrically erasable programmable read - only memory (EEPROM), erasable programmable read - only memory (EPROM), programmable read - only memory (PROM), read - only memory (ROM), magnetic memory, flash memory, magnetic disks or optical discs.

[0199] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0200] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above - mentioned peripheral interface module can be an output device, an input device, etc.

[0201] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0202] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server, and the above - mentioned processing component, storage component, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0203] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above - mentioned Figure 1 A real estate registration privacy protection method based on zero - knowledge proof in the shown embodiment.

[0204] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above - described systems, devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0205] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0206] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0207] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real estate registration privacy protection method based on zero-knowledge proof, characterized in that, Including: Collecting logistics trajectory data through multi-source sensors of a transportation carrier, encapsulating the logistics trajectory data into a physical carrier for warrant transfer, and generating a spatio-temporal coding sequence; Capturing dynamic voiceprint parameters based on the acoustic resonance effect in the enclosed space of the transportation carrier, synchronously encrypting the dynamic voiceprint parameters with the spatio-temporal nodes of the spatio-temporal coding sequence, and generating an encryption key that dynamically evolves along with the logistics path; Binding the geographical coordinates of the spatio-temporal coding sequence with the voiceprint parameters of the encryption key, and generating a joint fingerprint of the logistics trajectory and voiceprint features based on timestamp constraint conditions; Splitting the joint fingerprint into trajectory shards and voiceprint shards, performing logical relevance verification on the trajectory shards and voiceprint shards through a zero-knowledge proof protocol and secure multi-party computation, reorganizing the trajectory shards and voiceprint shards only when the timestamp constraint conditions are met to verify the consistency of real estate registration data, and isolating the complete path of the spatio-temporal coding sequence and the plaintext parameters of the encryption key.

2. The method according to claim 1, characterized in that, The generating of the joint fingerprint of the logistics trajectory and voiceprint features based on timestamp constraint conditions includes: Dividing the geographical coordinates of the spatio-temporal coding sequence into discrete geographical blocks according to the continuous timestamps of the logistics path, and each geographical block corresponds to the displacement range of the transportation carrier within a fixed time interval; Generating a voiceprint confusion factor synchronized with the geographical block based on the phase fluctuation of the voiceprint parameters of the encryption key within the corresponding time interval; Aliasing the longitude and latitude coordinates of the geographical block with the voiceprint confusion factor, and performing parameter interweaving on the coordinate values, voiceprint confusion factor, and corresponding timestamp of the geographical block based on chaos rules to generate a block confusion unit; According to the continuity constraint of the logistics path, performing hash chain association on the timestamp of the block confusion unit and the voiceprint confusion factor to generate a joint fingerprint of the logistics trajectory and voiceprint features.

3. The method according to claim 2, wherein The aliasing of the longitude and latitude coordinates of the geographical block with the voiceprint confusion factor, and performing parameter interweaving on the coordinate values, voiceprint confusion factor, and corresponding timestamp of the geographical block based on chaos rules to generate a block confusion unit includes: Decomposing the longitude and latitude coordinates of the geographical block into a longitude sequence and a latitude sequence, and generating a coordinate permutation sequence based on the enclosed space parameters of the transportation carrier; Extracting the frequency domain features and time domain periods of the voiceprint confusion factor according to the displacement range of the logistics path corresponding to the timestamp of the time window associated with the geographical block, and generating an interleaving weight vector; Performing bit-order offset on the longitude sequence, latitude sequence, and the interleaving weight vector, and performing iterative permutation on the offset coordinate components and voiceprint confusion factor based on a chaos initial perturbation value to generate a coordinate-voiceprint interleaving vector; Performing dynamic perturbation on the coordinate-voiceprint interleaving vector according to the movement direction of the transportation carrier, and cross-overlaying the perturbed coordinate-voiceprint interleaving vector with the coordinate permutation sequence to generate a block confusion unit.

4. The method according to claim 3, wherein The performing of iterative permutation on the offset coordinate components and voiceprint confusion factor based on a chaos initial perturbation value to generate a coordinate-voiceprint interleaving vector includes: Generate a dynamic chaotic seed associated with the chaotic initial perturbation value based on the resonant frequency of the enclosed space of the transport carrier and the displacement range of the logistics path; Arrange the offset coordinate components in timestamp order as an input vector, and inject the dynamic chaotic seed into the head and tail nodes of the input vector to generate a chaotic iterative input queue; Obtain the iterative step size of the chaotic mapping according to the real-time acceleration and vibration intensity of the transport carrier, and perform cyclic displacement and parameter replacement on the vector elements of the chaotic iterative input queue to generate a chaotic perturbation intermediate vector; Intercept the chaotic perturbation intermediate vector in segments according to the logistics path corresponding to the timestamp, and perform cross recombination on the longitude sequence, latitude sequence and voiceprint confusion factor to generate a coordinate voiceprint interleaved vector.

5. The method according to claim 1, characterized in that, The logical relevance verification of the trajectory shards and voiceprint shards through the zero-knowledge proof protocol and secure multi-party computation includes: Apply the speed change parameter of the transport carrier and the resonant frequency parameter of the enclosed space to the trajectory shards and voiceprint shards respectively to generate a trajectory challenge vector and a voiceprint challenge vector; Split the trajectory challenge vector and the voiceprint challenge vector into verification shard groups according to the constraint conditions of the timestamp, and distribute the confused trajectory shards and voiceprint shards to different registration agencies; Generate a response generation rule based on the real-time state parameters of the transport carrier, and synchronize the response generation rule among the registration agencies through secure multi-party computation; In the zero-knowledge proof protocol, perform segmented perturbation on the verification shard group according to the response generation rule to obtain local response shards, and the local response shards contain timestamp continuity identifiers; Perform cross-verification on the local response shards. Only when the identifier satisfies timestamp continuity and is consistent with the response generation rule, trigger shard recombination to verify the consistency of the real estate registration data.

6. The method according to claim 5, characterized in that, The generating the response generation rule based on the real-time state parameters of the transport carrier includes: Divide the real-time speed, acceleration and enclosed space vibration intensity of the transport carrier into a dynamic state sequence according to the timestamp, and extract the state parameter components corresponding to each timestamp; Generate a dynamic weight factor bound to the timestamp based on the amplitude fluctuation of the dynamic state sequence and the frequency domain offset of the voiceprint confusion factor, where the generation of the dynamic weight factor is constrained by the curvature radius of the logistics path; Interweave the dynamic weight factor with the trajectory shards and voiceprint shards corresponding to the timestamp to generate a rule template containing timestamp constraint conditions; Encrypt the rule template in shards through secure multi-party computation, distribute the encrypted rule shards to the registration agencies, and generate a response generation rule based on multi-party collaborative decryption.

7. The method according to claim 1, wherein The synchronously encrypting the dynamic voiceprint parameters and the spatio-temporal nodes of the spatio-temporal coding sequence to generate an encryption key that dynamically evolves with the logistics path includes: Perform phase fluctuation analysis on the dynamic voiceprint parameters, extract the phase offset sequence corresponding to its main frequency component, and quantize the integer sequence matching the timestamp bit width of the spatio-temporal nodes; Interweaving the latitude and longitude coordinates of the spatiotemporal coding sequence with the timestamp to generate a mixed coding block, and alternately flipping the bits of the mixed coding block based on the parity of the phase offset sequence to generate an initial key segment; Generate a chaotic perturbation vector based on a hash chain of continuous timestamps of the logistics path, perform a bitwise modular addition operation on the initial key segment and the chaotic perturbation vector, and generate an intermediate key segment with dynamic characteristics of the logistics path; The intermediate key segment is subjected to multiple rounds of displacement disturbance according to the energy attenuation curve of the voiceprint main frequency to generate an encryption key that dynamically evolves along the logistics path.

8. A real estate registration privacy protection method based on zero-knowledge proof, characterized in that, include: A collection module collects logistics trajectory data through multi-source sensors of the transport carrier, encapsulates the logistics trajectory data into a physical carrier for the transfer of the warrant, and generates a spatiotemporal coding sequence; An encryption module, which captures dynamic voiceprint parameters based on the acoustic resonance effect of the confined space of the transport carrier, encrypts the dynamic voiceprint parameters synchronously with the spatiotemporal nodes of the spatiotemporal coding sequence, and generates an encryption key that evolves dynamically with the logistics path; A generation module binds the geographic coordinates of the spatiotemporal coding sequence with the voiceprint parameters of the encryption key, and generates a joint fingerprint of the logistics trajectory and the voiceprint feature based on the timestamp constraint condition; A verification module splits the joint fingerprint into trajectory fragments and voiceprint fragments, performs logical correlation verification on the trajectory fragments and voiceprint fragments through a zero-knowledge proof protocol and secure multi-party computing, recombines the trajectory fragments and voiceprint fragments only when the timestamp constraint condition is met to verify the consistency of the real estate registration data, and isolates the complete path of the spatiotemporal coding sequence and the plaintext parameters of the encryption key.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a real estate registration privacy protection method based on zero-knowledge proof as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a computer, a real estate registration privacy protection method based on zero-knowledge proof as described in any one of claims 1 to 7 is implemented.

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