Real estate registration privacy protection method and system based on zero-knowledge proof

Abstract

The invention provides a real estate registration privacy protection method and system based on zero knowledge proof. The method comprises the following steps: firstly, collecting logistics track data by using a multi-source sensor, converting the logistics track data into a physical carrier for weight transfer, and meanwhile, generating a space-time coding sequence; a dynamic voiceprint parameter is obtained through a sound wave resonance effect in a closed space, and an encryption key changing along with a logistics path is generated through encryption in combination with a space-time coding sequence. And after the geographic coordinates are bound with the voiceprint parameters, creating a combined fingerprint of the logistics track and the voiceprint features based on a timestamp condition. The joint fingerprint is split into a track fragment and a voiceprint fragment, and the track fragment and the voiceprint fragment are recombined when a specific time constraint condition is met, so that the consistency of real estate registration data is ensured, and meanwhile, a complete path of a space-time coding sequence and plaintext information of an encryption key are isolated. According to the technical scheme provided by the invention, the efficiency and security of real estate registration privacy protection can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of privacy protection for real estate registration, and in particular to a method and system for privacy protection for real estate registration 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 critical technical requirement. Especially in logistics and transportation, when it comes to the physical carrier of the transfer of certificates, how to effectively collect and verify the authenticity of logistics trajectory data becomes a major challenge. In order to prevent forgery or tampering, a technology that can dynamically track the logistics process while protecting the privacy and security of all parties involved is needed. In addition, the technology must be able to adapt to different environmental conditions and have the ability to efficiently process large-scale data to support real-time verification and updates.

[0003] At present, real estate registration privacy protection mainly uses blockchain technology to record and verify logistics information. By recording logistics track data on an unalterable 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 logistics process can be automatically monitored and verified. This method not only improves efficiency and reduces human intervention, but also enhances system transparency and security, providing a reliable data foundation 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 erroneous data. Secondly, as the amount of logistics data continues to grow, the blockchain network may face scalability challenges, resulting in slower transaction speeds and higher costs. Finally, although the blockchain itself provides a high level of security, it still has deficiencies in user identity authentication and data privacy protection, especially in real estate registration scenarios involving sensitive information, which may lead to the risk of privacy leakage. Summary of the invention

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

[0006] In the first aspect, the present application provides a privacy protection method for real estate registration based on zero-knowledge proof, including: Collect logistics trajectory data through multi-source sensors of the transport carrier, encapsulate the logistics trajectory data into a physical carrier for the transfer of the warrant, and generate a spatiotemporal coding sequence; Capturing dynamic voiceprint parameters based on the acoustic resonance effect of the confined space of the transport carrier, encrypting the dynamic voiceprint parameters synchronously with the spatiotemporal nodes of the spatiotemporal coding sequence, and generating an encryption key that evolves dynamically along the logistics path; Binding the geographic coordinates of the spatiotemporal coding sequence with the voiceprint parameters of the encryption key, and generating a joint fingerprint of the logistics trajectory and the voiceprint feature based on the timestamp constraint condition; The joint fingerprint is split into trajectory fragments and voiceprint fragments, and the logical association verification is performed on the trajectory fragments and the voiceprint fragments through a zero-knowledge proof protocol and secure multi-party computing. The trajectory fragments and the voiceprint fragments are recombined only when the timestamp constraint condition is met to verify the consistency of the real estate registration data, and isolate the complete path of the spatiotemporal coding sequence and the plaintext parameters of the encryption key.

[0007] Optionally, generating a joint fingerprint of the logistics track and the voiceprint feature based on the timestamp constraint condition includes: The geographic coordinates of the spatiotemporal coding sequence are divided into discrete geographic blocks according to the continuous timestamps of the logistics path. Each geographic block corresponds to the displacement range of the transport carrier within a fixed time interval. generating a voiceprint confusion factor synchronized with the geographic block based on phase fluctuations of the voiceprint parameters of the encryption key within a corresponding time interval; The longitude and latitude coordinates of the geographic block are overlapped with the voiceprint confusion factor, and the coordinate value of the geographic block, the voiceprint confusion factor and the corresponding timestamp are interleaved based on a chaotic rule to generate a block confusion unit; According to the continuity constraint of the logistics path, the timestamp of the block confusion unit is associated with the voiceprint confusion factor in a hash chain to generate a joint fingerprint of the logistics track and the voiceprint feature.

[0008] Optionally, the overlapping of the longitude and latitude coordinates of the geographic block with the voiceprint confusion factor, interleaving the coordinate value of the geographic block, the voiceprint confusion factor and the corresponding timestamp based on a chaotic rule to generate a block confusion unit includes: Decomposing the longitude and latitude coordinates of the geographic block into a longitude sequence and a latitude sequence, and generating a coordinate replacement sequence based on the closed space parameters of the transport carrier; Extract the frequency domain characteristics and time domain period of the voiceprint confusion factor according to the logistics path displacement range corresponding to the timestamp of the time window associated with the geographic block, and generate an interleaving weight vector; The longitude sequence, the latitude sequence and the interleaving weight vector are shifted in order, and the shifted coordinate components and voiceprint confusion factors are iteratively permuted based on the chaotic initial disturbance value to generate a coordinate voiceprint interleaving vector; The coordinate voiceprint interleaved vector is dynamically disturbed according to the moving direction of the transport carrier, and the disturbed coordinate voiceprint interleaved vector is cross-superimposed with the coordinate permutation sequence to generate a block confusion unit.

[0009] Optionally, the iterative permutation of the offset coordinate components and the voiceprint confusion factor based on the chaotic initial disturbance value to generate the coordinate voiceprint interleaved vector includes: Based on the confined space resonance frequency of the transport carrier and the displacement range of the logistics path, a dynamic chaos seed associated with the initial chaos disturbance value is generated; Arranging the offset coordinate components in timestamp order as an input vector, and injecting the dynamic chaos seed into the first and last nodes of the input vector to generate a chaos iteration input queue; According to the real-time acceleration and vibration intensity of the transport carrier, the iteration step length of the chaotic mapping is obtained, and the vector elements of the chaotic iteration input queue are cyclically shifted and parameter replaced to generate a chaotic disturbance intermediate vector; The chaotic disturbance intermediate vector is segmented according to the logistics path corresponding to the timestamp, and the longitude sequence, latitude sequence and voiceprint confusion factor are cross-recombined to generate a coordinate voiceprint interleaved vector.

[0010] Optionally, the performing logical correlation verification on the trajectory slice and the voiceprint slice through a zero-knowledge proof protocol and secure multi-party computing includes: Applying the speed change parameter of the transport carrier and the resonance frequency parameter of the enclosed space to the trajectory slice and the voiceprint slice respectively to generate a trajectory challenge vector and a voiceprint challenge vector; Splitting the trajectory challenge vector and the voiceprint challenge vector into verification fragment groups according to the constraints of the timestamp, and distributing the obfuscated trajectory fragments and voiceprint fragments to different registration agencies; generating response generation rules based on the real-time status parameters of the transport carrier, and synchronizing the response generation rules among registration institutions through secure multi-party computing; In the zero-knowledge proof protocol, segmented perturbation is performed on the verification shard group according to the response generation rule to obtain a partial response shard, wherein the partial response shard includes a timestamp continuity identifier; The local response shards are cross-validated, and only when the identifiers satisfy the timestamp continuity and are consistent with the response generation rules, shard reorganization is triggered to verify the consistency of the real estate registration data.

[0011] Optionally, the generating a response generation rule based on the real-time status parameter of the transport carrier includes: The real-time speed, acceleration and confined space vibration intensity of the transport carrier are divided into a dynamic state sequence according to the timestamp, and the state parameter component corresponding to each timestamp is extracted; Based on the amplitude fluctuation of the dynamic state sequence and the frequency domain offset of the voiceprint confusion factor, a dynamic weight factor bound to the timestamp is generated, wherein the generation of the dynamic weight factor is subject to the curvature radius of the logistics path as a constraint condition; Interweaving the dynamic weight factor with the trajectory slice and the voiceprint slice corresponding to the timestamp to generate a rule template containing the timestamp constraint condition; The rule template is encrypted in slices through secure multi-party computing, the encrypted rule slices are distributed to the registration agency, and response generation rules are generated based on multi-party collaborative decryption.

[0012] Optionally, encrypting the dynamic voiceprint parameters synchronously with the spatiotemporal nodes of the spatiotemporal coding sequence to generate an encryption key that evolves dynamically along the logistics path includes: Performing phase fluctuation analysis on the dynamic voiceprint parameters, extracting the phase offset sequence corresponding to the main frequency component thereof, and quantizing an integer sequence matching the bit width of the spatiotemporal node timestamp; 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.

[0013] In the second aspect, the present application provides a real estate registration privacy protection system based on zero-knowledge proof, including: 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.

[0014] In a third aspect, the present application provides a computing device comprising 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.

[0015] In a fourth aspect, the present application provides a computer storage medium storing a computer program. 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.

[0016] In an embodiment of the present application, logistics trajectory data is collected through multi-source sensors of a transport carrier, the logistics trajectory data is encapsulated as a physical carrier for certificate transfer, and a space-time coding sequence is generated; dynamic voiceprint parameters are captured based on the acoustic resonance effect of the enclosed space of the transport carrier, the dynamic voiceprint parameters are synchronously encrypted with the space-time nodes of the space-time coding sequence, and an encryption key that dynamically evolves with the logistics path is generated; the geographic coordinates of the space-time coding sequence are bound to the voiceprint parameters of the encryption key, and a joint fingerprint of the logistics trajectory and voiceprint features is generated based on a timestamp constraint; the joint fingerprint is split into trajectory fragments and voiceprint fragments, and a logical association verification is performed on the trajectory fragments and voiceprint fragments through a zero-knowledge proof protocol and secure multi-party computing, and the trajectory fragments and voiceprint fragments are recombined only when the timestamp constraint is met to verify the consistency of the real estate registration data, and the complete path of the space-time coding sequence and the plaintext parameters of the encryption key are isolated.

[0017] The technical solution of this application has the following beneficial effects: This application collects logistics information through multi-source sensors, converts it into a physical carrier for the transfer of warrants, and generates a spatiotemporal coding sequence at the same time. 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 transport carrier, and the spatiotemporal coding sequence is synchronously encrypted to generate an encryption key that evolves as the logistics path changes. This process enhances the security of the encryption key, making it difficult to predict or crack. Bind the geographic coordinates to the voiceprint parameters in the encryption key, and generate a joint fingerprint based on the timestamp condition. This operation ensures the uniqueness and non-tamperability of the logistics track and voiceprint features, and further strengthens data security protection. The joint fingerprint is split into track fragments and voiceprint fragments, and the logical association is verified through zero-knowledge proof protocol and secure multi-party computing. This approach maximizes privacy protection and avoids the leakage of sensitive information while ensuring the accuracy of verification.

[0018] Furthermore, based on the timestamp constraint, this method first divides the geographic coordinates of the spatiotemporal coding sequence into discrete geographic blocks according to the continuous timestamps on the logistics path, and generates voiceprint confusion factors according to the phase fluctuations of the voiceprint parameters of the encryption key in each time interval. Then, the longitude and latitude coordinates are overlapped with the voiceprint confusion factors and interwoven into block confusion units according to the chaos rules. Finally, according to the continuity requirements of the logistics path, the timestamps and voiceprint confusion factors of the block confusion units are associated by hash chain to form a joint fingerprint of the logistics trajectory and voiceprint features. This method not only improves the complexity and security of the logistics trajectory data and 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, the integrity and consistency of the data are effectively maintained, while supporting an efficient and privacy-friendly verification mechanism.

[0019] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flowchart of a real estate registration privacy protection method based on zero-knowledge proof provided by the present application is shown; Figure 2 A structural diagram of a real estate registration privacy protection system based on zero-knowledge proof provided by the present application is shown; Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0023] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0024] This scheme proposes a method to collect logistics trajectory data based on multi-source sensors, which supports privacy protection in the real estate registration process by encapsulating these data into physical carriers and generating spatiotemporal coding sequences. This method aims to ensure the secure transmission and verification of logistics information, while using zero-knowledge proof protocols to ensure data consistency and privacy.

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0026] Figure 1 A flowchart of a real estate registration privacy protection method based on zero-knowledge proof is provided for the embodiment of the present application, such as Figure 1 As shown, the method includes: 101. Collect logistics trajectory data through multi-source sensors of the transport carrier, encapsulate the logistics trajectory data into a physical carrier for the transfer of the warrant, and generate a spatiotemporal coding sequence; In this step, the logistics trajectory data includes information such as position coordinates and speed changes collected by various sensors such as global positioning systems and accelerometers during the transportation process, which is used to record the specific path of the transport carrier during the logistics process.

[0027] The physical carrier of the certificate transfer is the physical or digital form formed after the logistics trajectory data is standardized, which is used to prove the transfer process of real estate ownership.

[0028] The spatiotemporal coding sequence is a structured data format that combines timestamps and geographic coordinates. It is used to record in detail the time and location information of each node in the logistics path, ensuring that the entire logistics process is transparent and traceable.

[0029] In the embodiment of the present application, first, multi-source sensors are used to collect the logistics trajectory data of the transport carrier in real time, including but not limited to position coordinates, speed and other parameters. Then, these raw data are standardized and converted into a data set in a unified format. Then, 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 the certificate. Finally, a spatiotemporal coding sequence is generated according to each time node on the logistics path and the corresponding geographical location to ensure the transparency and verifiability of the entire logistics process.

[0030] In a specific real estate transaction case, the seller used a transport vehicle equipped with a global positioning system and accelerometer to transport important documents. The system automatically recorded all position changes and speed changes from the departure point to the destination, forming detailed logistics trajectory data, and packaged it into a legally binding electronic document as the physical carrier of the certificate transfer. At the same time, based on the time and location information recorded during the transportation process, a detailed time-space coding sequence was generated as effective evidence of this logistics activity.

[0031] 102. Capturing dynamic voiceprint parameters based on the acoustic resonance effect of the confined space of the transport carrier, encrypting the dynamic voiceprint parameters synchronously with the spatiotemporal nodes of the spatiotemporal coding sequence, and generating an encryption key that evolves dynamically along the logistics path; In this step, the acoustic resonance effect refers to the phenomenon of interaction of sound waves generated by vibration in a confined space, which can be used to capture dynamic voiceprint parameters, that is, sound characteristics that change with time and space.

[0032] Dynamic voiceprint parameters are sound features obtained based on the sound wave resonance effect and are used for encryption.

[0033] The encryption key is a security code generated by synchronizing dynamic voiceprint parameters with the space-time nodes in the space-time coding sequence. It is designed to protect data from unauthorized access, and its content will be dynamically updated as the logistics path changes.

[0034] In the embodiment of the present application, first, an acoustic wave sensor is set inside the transport carrier to capture the dynamic soundprint parameters generated by the acoustic wave resonance effect. Then, these soundprint parameters are analyzed to extract the main frequency components and phase offset sequences. Then, according to the time nodes in the spatiotemporal coding sequence, the dynamic soundprint parameters are synchronized and encrypted to form an encryption key that evolves with the logistics path. Ultimately, this encryption method not only reflects the characteristics of the logistics route, but also enhances data security.

[0035] Continuing with the previous example, during transportation, the system monitored the acoustic environment inside the vehicle and extracted a series of unique voiceprint features from it. After arriving at the destination, a unique encryption key was generated based on these voiceprint features and the timestamp on the logistics path. This key is not only closely related to the logistics track, but also greatly improves the security of real estate registration file transmission.

[0036] 103. Binding the geographic coordinates of the spatiotemporal coding sequence with the voiceprint parameters of the encryption key, and generating a joint fingerprint of the logistics trajectory and the voiceprint feature based on the timestamp constraint condition; In this step, the geographic coordinates are data indicating the specific location of an object on the surface of the earth, and are usually composed of longitude and latitude.

[0037] The voiceprint parameters of the encryption key are sound features obtained based on the acoustic resonance effect and are used for encryption.

[0038] The joint fingerprint is a security identifier generated based on timestamp constraints by binding the geographic coordinates in the spatiotemporal coding sequence with the voiceprint parameters in the encryption key. It is used to verify the authenticity and integrity of the logistics trajectory and prevent any unauthorized tampering.

[0039] In the embodiment of the present application, first, the geographic coordinate information is extracted from the spatiotemporal coding sequence. Then, these geographic coordinates are combined with the voiceprint parameters in the encryption key, and the binding operation is performed according to the timestamp constraint. Then, complex algorithms and technical means are used to ensure that the created joint fingerprint can accurately reflect the logistics trajectory and effectively prevent tampering. Finally, the generated joint fingerprint not only contains the information of the logistics path, but also verifies its authenticity.

[0040] During the real estate registration process, the system builds a joint fingerprint based on the geographic coordinates and encryption keys generated in the previous step. This fingerprint not only proves the exact transportation path of the document, but also any attempt to modify the path information will be discovered due to fingerprint discrepancies, thus ensuring the security and reliability of the transaction.

[0041] 104. Split the joint fingerprint into trajectory fragments and voiceprint fragments, perform logical correlation verification on the trajectory fragments and voiceprint fragments through zero-knowledge proof protocol and secure multi-party computing, reassemble 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 isolate the complete path of the spatiotemporal coding sequence and the plaintext parameters of the encryption key.

[0042] In this step, the zero-knowledge proof protocol allows one party to verify the authenticity of the other party’s statement without revealing more information.

[0043] Secure multi-party computation is a method to protect the privacy of multiple parties when performing computations between them.

[0044] Logical correlation verification is to confirm the relationship between trajectory shards and voiceprint shards to ensure the consistency and integrity of the data.

[0045] Timestamp constraints are requirements that ensure that all operations are completed within a specific time frame.

[0046] In the embodiment of the present application, first, the joint fingerprint is split into a track slice and a voiceprint slice. Then, the zero-knowledge proof protocol and secure multi-party computing technology are applied to perform logical correlation verification on the two slices. Then, only when the timestamp constraint is met, the slices are reassembled for consistency check. Finally, the complete path of the spatiotemporal coding sequence and the plaintext parameters of the encryption key are isolated to ensure that only legitimate users can access the real information, maintaining the security and privacy of the data.

[0047] In the final verification stage of real estate registration, the registration agency receives encrypted information from the seller. By applying zero-knowledge proof protocol and secure multi-party computing, the authenticity of the logistics track is successfully verified, while maintaining a high degree of confidentiality of the information, ensuring the smooth completion of the transaction, while also protecting the privacy of all parties from infringement.

[0048] In summary, steps 101 to 104 provide a highly secure and privacy-protected real estate registration method. Through the comprehensive application of multiple advanced technologies such as multi-source sensor acquisition, voiceprint encryption, joint fingerprint generation, and zero-knowledge proof, not only the authenticity and integrity of logistics trajectory data are guaranteed, but also the data security and user privacy protection level are greatly improved, ensuring that every link in the real estate transaction process is effectively monitored and protected.

[0049] In order to further improve the security and unique identification of logistics tracks and voiceprint features, the solution describes in detail how to generate a joint fingerprint of logistics tracks and voiceprint features based on timestamp constraints. This process includes dividing geographic blocks, generating voiceprint confusion factors, aliasing geographic coordinates and voiceprint parameters, and finally generating a joint fingerprint through hash chain association, thereby enhancing data complexity and security, ensuring that the data cannot be tampered with and is difficult to forge. In some embodiments, the generation of a joint fingerprint of logistics tracks and voiceprint features based on timestamp constraints in step 103 includes: 201. Divide the geographic coordinates of the spatiotemporal coding sequence into discrete geographic blocks according to the continuous timestamps of the logistics path, and each geographic block corresponds to the displacement range of the transport carrier within a fixed time interval; In step 201, the spatiotemporal coding sequence refers to the result of digitally encoding the time and geographic coordinates of each node in the logistics process. A geographic block is a region with a fixed time interval divided according to continuous timestamps, and each geographic block corresponds to the geographic range covered by the transport carrier in a specific time period. The spatiotemporal coding sequence contains the timestamps and corresponding geographic coordinate data of all nodes on the logistics path, through which each position change in the logistics process can be accurately described. The role of the geographic block is to divide the continuous logistics path into a series of discrete parts for subsequent processing.

[0050] In the embodiment of the present application, the timestamps and corresponding geographic coordinate data of each node on the logistics path are first collected. Then, these data are segmented according to the set time interval, and all geographic coordinate points in each period of time form a geographic block. Then, the division of geographic blocks is realized through an algorithm to ensure that each geographic block represents the displacement range of the transport carrier within a fixed time interval. Finally, the discretization processing of geographic information is realized, providing a basis for subsequent steps.

[0051] 202. Generate a voiceprint confusion factor synchronized with the geographic block based on phase fluctuations of the voiceprint parameters of the encryption key within a corresponding time interval; 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 over time during the propagation of the sound wave. The voiceprint confusion factor is a random perturbation factor based on the voiceprint characteristics introduced to increase security. The voiceprint parameters contain the unique properties of the sound signal and can be used to identify and verify the identity. The phase fluctuation reflects the subtle changes in the sound signal during the transmission process. This change can be used to generate a unique voiceprint confusion factor to enhance the security of the data.

[0052] In the embodiment of the present application, the phase fluctuation value of the voiceprint parameter in each time interval is first calculated using a pre-set encryption algorithm. Then, these fluctuation values ​​are used as the voiceprint confusion factor in the time period. Then, an additional security protection layer is added on the basis of the geographic block, so that even if the geographic location information is intercepted, accurate information cannot be parsed without the correct voiceprint confusion factor. Finally, the security protection capability of the entire system is improved.

[0053] 203. Mixing the longitude and latitude coordinates of the geographic block with the voiceprint confusion factor, interweaving the coordinate value of the geographic block, the voiceprint confusion factor and the corresponding timestamp based on a chaotic rule to generate a block confusion unit; In step 203, the longitude and latitude coordinates represent the specific location of the geographic block, and 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 by complex transformation of the geographic block coordinates, voiceprint confusion factors and timestamps. The longitude and latitude coordinates define the spatial location of the geographic block, and the chaos rule ensures the high unpredictability of the data transformation process. The block confusion unit is the product of the combination of the above elements, ensuring the security and uniqueness of the data.

[0054] In the embodiment of the present application, a suitable chaotic system is first selected to perform nonlinear transformation on the input data through its dynamic behavior. Then, the latitude and longitude coordinates of the geographic block, the voiceprint confusion factor and the corresponding timestamp are interleaved using techniques such as chaotic mapping. 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.

[0055] 204. According to the continuity constraint of the logistics path, the timestamp of the block confusion unit is associated with the voiceprint confusion factor in a hash chain to generate a joint fingerprint of the logistics track and the voiceprint feature.

[0056] In step 204, hash chain association refers to a method of linking data with different timestamps in a chain using a hash function to ensure the integrity and order of the data. The joint fingerprint is a unique identifier that combines the logistics track and voiceprint features. The hash chain association not only ensures the integrity of the data, but also maintains the logical order between the data. The joint fingerprint provides a unique basis for verification.

[0057] In the embodiment of the present application, firstly, according to the continuity principle of the logistics path, the timestamp and voiceprint confusion factor of each block confusion unit are hashed in turn. Then, they are linked in chronological order to form an unalterable link. Then, a joint fingerprint containing the logistics track and voiceprint features is generated in this way. Finally, the one-way and anti-collision properties of the hash function are utilized to ensure the security and reliability of the joint fingerprint.

[0058] Here is a specific example: In a real estate transaction case, the seller wants to ensure the authenticity and security of logistics documents. First, the seller collects and encodes the timestamps and geographic coordinates of each key node in the logistics process. Then, for each period of time, the unique voiceprint confusion factor is calculated to enhance data confidentiality. Subsequently, the chaos rule is used to perform aliasing on the geographic coordinates, voiceprint confusion factor and timestamp to create a secure block confusion unit. Finally, all block confusion units are connected into a string through the hash chain association method to form a unique joint fingerprint to ensure the authenticity and integrity of the logistics documents.

[0059] In summary, steps 201 to 204 construct an efficient and secure joint fingerprint mechanism by combining timestamp, geographic coordinates, voiceprint features and chaos theory, which not only improves the anti-counterfeiting ability of logistics tracks, but also enhances the security of 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, such as financial transactions, important document delivery, etc.

[0060] In order to solve the problem of geographic block information confusion and protection, the scheme adopts chaotic rules to interleave parameters and generate block confusion units. This step increases the complexity of data processing by introducing dynamic perturbation and cross-overlapping technology, and effectively improves the level of data security protection. In some embodiments, the latitude and longitude coordinates of the geographic block and the voiceprint confusion factor are overlapped in step 203, and the coordinate value of the geographic block, the voiceprint confusion factor and the corresponding timestamp are interleaved based on chaotic rules to generate a block confusion unit, including: 301. Decomposing the longitude and latitude coordinates of the geographic block into a longitude sequence and a latitude sequence, and generating a coordinate replacement sequence based on the closed space parameters of the transport carrier; In step 301, the longitude and latitude coordinates of the geographic block are understood as a data pair describing a specific geographic location, consisting of longitude and latitude. The transport carrier confined space parameters refer to a series of values ​​set according to the internal environmental characteristics of the transport vehicle, which are used to adjust the coordinate replacement method. The coordinate replacement sequence is a new coordinate sequence obtained by the above parameter transformation, which is used to increase the security of the coordinate data.

[0061] In the embodiment of the present application, the longitude and latitude coordinates of the geographic block are first decomposed into two independent sequences, namely the longitude sequence and the latitude sequence. Then, an algorithm model is generated based on the parameters of the confined space of the transport carrier, and the model can rearrange the coordinates according to the preset rules to generate a coordinate permutation sequence. The final result is a processed coordinate sequence that is difficult to directly identify the original position information.

[0062] 302. Extract the frequency domain characteristics and time domain period of the voiceprint confusion factor according to the logistics path displacement range corresponding to the timestamp of the time window associated with the geographic block, and generate an interleaving weight vector; In step 302, the time window refers to the time range associated with the geographic block, and the timestamp is the data identifying a specific time point. The logistics path displacement range represents the distance interval that the item moves during the logistics process. The frequency domain characteristics and time domain period of the voiceprint confusion factor are the characteristic performances of the sound signal in the frequency dimension and time dimension respectively. The interleaving weight vector is generated based on these characteristics and is a set of values ​​used to adjust the degree of interleaving between different data.

[0063] In the embodiment of the present application, the time window corresponding to the geographic block is first determined, and the displacement range of the logistics path within the time period is extracted. Subsequently, the frequency domain characteristics and time domain period of the voiceprint confusion factor are analyzed, and the interleaving weight vector is calculated using a specific algorithm. This process utilizes signal processing technology to ensure that the generated interleaving weight vector can effectively reflect the complex relationship between the data, and finally the interleaving weight vector is generated for use in subsequent steps.

[0064] 303. Performing a position shift on the longitude sequence, the latitude sequence and the interleaving weight vector, iteratively replacing the offset coordinate components and the voiceprint confusion factor based on the chaotic initial disturbance value, and generating a coordinate voiceprint interleaving vector; In step 303, the longitude sequence and the latitude sequence refer to two independent data sequences formed after the longitude and latitude coordinates of the geographic block are decomposed. The interleaving weight vector is a set of numerical values ​​generated based on the voiceprint confusion factor in the previous step, which is used to adjust the degree of interleaving between different data. Bit sequence shift refers to the process of adjusting the position of elements in a sequence. The chaotic initial perturbation value is a random number generated based on chaos theory, which is used to increase the complexity and security of the encryption process. The coordinate component refers to the specific value of longitude or latitude. The coordinate voiceprint interleaving vector is a new vector that integrates geographic location information and voiceprint features after iterative permutation processing.

[0065] In the embodiment of the present application, the longitude sequence and latitude sequence obtained in step 301 and the interleaved weight vector generated in step 302 are first subjected to a position shift process, and the positional relationship of the internal elements of these sequences is changed through a specific algorithm to preliminarily disrupt the original structure. Next, the chaotic initial disturbance value is used as an input parameter to iteratively permute the coordinate components (i.e., longitude and latitude values) and the voiceprint confusion factor after the position shift. In this process, an algorithm based on chaotic mapping is used to ensure that each iteration can produce highly random and unpredictable results. Finally, the above processing results are integrated to form a coordinate voiceprint interleaved vector, realizing the effective fusion and protection of geographic information and voiceprint features.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] Here is a specific example: 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.

[0070] In summary, steps 301 to 304 not only effectively hide the original location information but also enhance the security of data transmission by performing multi-level transformation and encryption on the geographic block information. The entire process combines modern cryptographic principles and signal processing technology, providing strong technical support for various application scenarios that require confidentiality. In particular, step 303, by introducing chaos theory, increases the difficulty of cracking and further improves the robustness and anti-attack capability of the system.

[0071] In order to further solve the problem of insufficient data security in the process of encrypting geographic block information, this solution deeply explores the process of iteratively replacing coordinate components and voiceprint confusion factors based on chaotic initial disturbance values, emphasizes the use of parameters such as the resonance frequency of the closed space of the transport carrier and real-time acceleration to generate dynamic chaotic seeds, and realizes the generation of coordinate voiceprint interleaving vectors through cyclic displacement and parameter replacement, thereby strengthening the dynamic evolution characteristics of the encryption key. In some embodiments, the iterative replacement of the offset coordinate components and voiceprint confusion factors based on the chaotic initial disturbance value in step 304 to generate the coordinate voiceprint interleaving vector includes: 401. Generate a dynamic chaos seed associated with the initial chaos disturbance value based on the resonance frequency of the confined space of the transport carrier and the displacement range of the logistics path; In step 401, the confined space resonance frequency of the transport carrier refers to an inherent vibration frequency determined according to the internal structural characteristics of the transport vehicle. The logistics path displacement range represents the distance interval that the item moves during the entire logistics process. The dynamic chaos seed is a random number sequence generated based on the above two parameters and the initial chaos disturbance value, which is used in the subsequent data encryption process.

[0072] In the embodiment of the present application, the resonance frequency of the confined space of the transport carrier is first measured, and the corresponding value is calculated in combination with the displacement range of the logistics path. Then, these values ​​and the initial disturbance value of chaos are used to generate dynamic chaos seeds through a specific algorithm. This process uses a method combining signal processing technology with chaos theory to ensure that the generated dynamic chaos seeds have a high degree of randomness and unpredictability. Finally, a dynamic chaos seed that can effectively enhance data security is obtained.

[0073] 402. Arrange the offset coordinate components in timestamp order as an input vector, and inject the dynamic chaos seed into the first and last nodes of the input vector to generate a chaos iteration input queue; In step 402, the offset coordinate component refers to the specific value of the longitude or latitude after the position order is adjusted. The time stamp sequence arrangement refers to sorting the data according to the time sequence of the event. The chaotic iteration input queue is a new data sequence formed after the dynamic chaotic seed is injected into the first and last nodes of the input vector sorted by the timestamp.

[0074] In the embodiment of the present application, the offset coordinate components are first sorted according to the corresponding timestamps to form an input vector. Then, the dynamic chaotic seeds generated in the above steps are added to the start and end positions of the input vector respectively to construct a chaotic iterative input queue. Here, a simple list operation is used to achieve data reorganization, ensuring the security and integrity of the data. Finally, a chaotic iterative input queue containing the original data and additional encrypted elements is formed.

[0075] 403. According to the real-time acceleration and vibration intensity of the transport carrier, the iteration step length of the chaotic mapping is obtained, and the vector elements of the chaotic iteration input queue are cyclically shifted and parameter replaced to generate a chaotic disturbance intermediate vector; In step 403, the real-time acceleration and vibration intensity of the transport carrier reflect the dynamic changes of the vehicle during driving. The iteration step of the chaotic mapping is a value calculated based on 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 cyclic displacement and parameter replacement of the chaotic iteration input queue.

[0076] In the embodiment of the present application, the real-time acceleration and vibration intensity of the transport carrier are first monitored, and the iterative step length of the chaotic mapping is determined by analyzing these data. Then, the chaotic iterative input queue is subjected to cyclic displacement and parameter replacement operations using this step length. Complex mathematical operations are specifically implemented to change the arrangement of the original data, increasing the difficulty of cracking. Finally, a chaotic perturbation intermediate vector is generated that contains both the original geographic information and the dynamic perturbation characteristics.

[0077] 404. The chaotic disturbance intermediate vector is segmented according to the logistics path corresponding to the timestamp, and the longitude sequence, latitude sequence and voiceprint confusion factor are cross-recombined to generate a coordinate voiceprint interleaved vector.

[0078] In step 404, the chaotic disturbance intermediate vector refers to the data sequence processed in the previous step, which includes the offset coordinate component and the influence of the dynamic chaotic seed. The logistics path segment corresponding to the timestamp refers to the different path segments divided according to the movement of the items in different time periods during the logistics process. Cross-recombination is the process of recombining the longitude sequence, latitude sequence and voiceprint confusion factor according to certain rules, with the purpose of generating an encrypted data structure that contains both geographic location information and voiceprint features and a coordinate voiceprint interleaved vector.

[0079] In the embodiment of the present application, the part of the chaotic disturbance intermediate vector related to the specific timestamp is first identified and intercepted, and this part corresponds to a segment in the logistics path. Then, for each segment, the longitude sequence, latitude sequence and voiceprint confusion factor in the segment are cross-recombined according to a preset algorithm. Here, a method based on genetic algorithm or neural network can be used to achieve effective fusion between different data by adjusting parameter weights. Finally, by processing the entire chaotic disturbance intermediate vector in this way, a complex and difficult to reverse-parse coordinate voiceprint interleaved vector is generated to ensure the security of geographic information and voiceprint features.

[0080] Here is a specific example: 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, proceed according to the method of steps 401 to 404. First, a dynamic chaos seed is generated based on the confined space resonance frequency of the transport carrier and the displacement range of the logistics path. Then, the dynamic chaos seed is added to form a chaos iteration input queue. Then, the iteration step of the chaos mapping is determined according to the real-time acceleration and vibration intensity of the transport carrier, and the chaos iteration input queue is processed to generate a chaotic disturbance intermediate vector. Finally, the chaotic disturbance intermediate vector is segmented according to the logistics path to generate a coordinate voiceprint interleaved vector. For example, in a specific time period, the logistics path shows that there are specific transportation activities in the area where the real estate is located, and the data of this time period is specially processed to add an additional security layer, so that even if some data fragments are obtained, it is difficult to restore the complete geographic location information.

[0081] In summary, steps 401 to 404 not only effectively hide the location information of the geographic block through a series of complex data transformation and encryption methods, but also greatly improve the security of the data transmission process. The entire process is rigorously designed, combining modern cryptographic principles, signal processing technology and the application of physical parameters, providing strong technical support for various application scenarios that require confidentiality. In particular, for areas such as real estate transactions involving sensitive information, data protection capabilities and privacy protection levels have been significantly improved.

[0082] In order to solve the problem of logical correlation verification between geographic block information and voiceprint data, the solution introduces a specific method for performing logical correlation verification of trajectory sharding and voiceprint sharding through zero-knowledge proof protocol and secure multi-party computing, focusing on applying speed change and resonance frequency parameters to generate challenge vectors, and synchronous response generation rules based on real-time state parameters to ensure data consistency and privacy protection while improving verification efficiency. In some embodiments, the logical correlation verification of the trajectory sharding and voiceprint sharding through zero-knowledge proof protocol and secure multi-party computing in step 104 includes: 501. Apply a speed change parameter of the transport carrier and a resonance frequency parameter of the enclosed space to the trajectory slice and the voiceprint slice respectively to generate a trajectory challenge vector and a voiceprint challenge vector; In step 501, the speed change parameter of the transport carrier refers to a series of values ​​determined according to the change of the speed of the transport vehicle during its travel. The confined space resonance frequency parameter is the natural vibration frequency calculated based on the internal structural characteristics of the transport vehicle. The trajectory challenge vector and voiceprint challenge vector are data sequences generated by applying the above parameters to the trajectory slice and voiceprint slice respectively, which are used in the subsequent security verification process.

[0083] In the embodiment of the present application, the speed change parameters of the transport carrier and the resonance frequency parameters of the confined space are first collected, and then the trajectory slices and voiceprint slices are processed using these parameters to generate corresponding challenge vectors. Signal processing technology is used here in combination with a specific algorithm to adjust the original data to ensure that the generated challenge vector can reflect the impact of the physical environment on the data. The final result is two new sets of data sequences, namely the trajectory challenge vector and the voiceprint challenge vector, which contain both the original information and an additional layer of security.

[0084] 502. Split the trajectory challenge vector and the voiceprint challenge vector into verification fragment groups according to the constraints of the timestamp, and distribute the obfuscated trajectory fragments and voiceprint fragments to different registration agencies; In step 502, the timestamp constraint refers to the standard for sorting data in the chronological order of events. The verification slice group is a data set after the trajectory challenge vector and the voiceprint challenge vector are split and reassembled according to the timestamp. The obfuscated trajectory slice and voiceprint slice refer to encrypted data fragments to prevent unauthorized access.

[0085] In the embodiment of the present application, the trajectory challenge vector and the voiceprint challenge vector are first split into multiple small segments according to the constraints of the timestamp to form a verification slice group. Then, these slices are obfuscated through the security protocol, and the obfuscated slices are distributed to different registration agencies. This process involves complex mathematical operations and data encryption technology to ensure the security of data transmission. Finally, a distributed data structure that is difficult to reverse parse is formed, which improves the security of the overall system.

[0086] 503. Generate a response generation rule based on the real-time status parameters of the transport carrier, and synchronize the response generation rule among registration institutions through secure multi-party computing; In step 503, the real-time state parameters of the transport carrier include dynamic information such as speed and direction. The response generation rules are guidelines formulated based on these state parameters and are used to synchronize the response mechanisms between different registration agencies. Secure multi-party computation is a technology that allows all parties to jointly perform computing tasks without disclosing their respective data privacy.

[0087] In the embodiment of the present application, the real-time status parameters of the transport carrier are first monitored, and response generation rules are generated accordingly. Then, these rules are synchronized between different registration agencies through secure multi-party computing technology. This step utilizes advanced cryptographic algorithms and distributed computing frameworks to ensure that the agencies can work together without leaking sensitive information. Ultimately, the ability of all participants to process data according to unified standards is realized.

[0088] 504. In the zero-knowledge proof protocol, perform segmented perturbation on the verification shard group according to the response generation rule to obtain a partial response shard, wherein the partial response shard includes a timestamp continuity identifier; In step 504, the zero-knowledge proof protocol is a technology that allows one party (verifier) ​​to verify the statement of another party (prover) without obtaining any useful information. The response generation rules are a set of algorithms or logic based on the real-time state parameters of the transport carrier to guide how to process data to generate a valid response. Segment perturbation refers to the process of locally adjusting and encrypting data to protect data privacy while ensuring its consistency. The local response fragment is a data fragment generated after the above process, which contains a timestamp continuity identifier for subsequent verification.

[0089] In an embodiment of the present application, a specific processing flow is first defined according to the response generation rules. Then, under the framework of the zero-knowledge proof protocol, a segmented perturbation operation is performed on each verification shard group. Specific encryption algorithms and logical operations are used here 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, each of which contains a timestamp continuity identifier to verify the time order consistency of the data. The final result is a data structure that protects the privacy of the original data and can verify consistency.

[0090] 505. Cross-validate the local response shards, and trigger shard reorganization to verify the consistency of the real estate registration data only when the identifier satisfies the timestamp continuity and is consistent with the response generation rule.

[0091] In step 505, cross-validation refers to the process of comparing data from different sources to confirm their consistency and authenticity. The timestamp continuity identifier is a mark added to the partial response shard in the previous step, which is used to identify whether the time series corresponding to the shard is continuous. Shard reorganization is to merge the verified partial response shards to restore the complete data set and verify the consistency of the real estate registration data.

[0092] In the embodiment of the present application, all local response shards are first cross-validated to check whether the timestamp continuity identifier in each shard meets expectations and is compared with the response generation rules. If all identifiers meet the continuity requirements and are consistent with the rules, the shard reorganization process is triggered. In this process, a specific algorithm is used to reassemble the scattered data into a whole. This process not only verifies the time sequence consistency 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, which ensures the security and reliability of the entire transaction process.

[0093] Here is a specific example: In a specific real estate transaction case, the seller wants to ensure the consistency and security between the geographic location information and voiceprint data provided by it. After completing the previous steps, continue the operation according to the method of steps 501 to 505. First, the trajectory challenge vector and the voiceprint challenge vector are generated according to the speed change of the transport carrier and the resonance frequency of the confined space. Next, these vectors are split by timestamp and the obfuscated data is distributed to different registration agencies. Then, response generation rules are formulated based on the real-time status parameters of the transport carrier and these rules are synchronized through secure multi-party computing. Next, segmented perturbations are performed on the verification shard group under the zero-knowledge proof protocol to obtain local response shards containing timestamp continuity identifiers. Finally, when the identifier satisfies the timestamp continuity and is consistent with the response generation rule, the shard reorganization is triggered to verify the consistency of the real estate registration data. The result of this is to ensure high security and data consistency throughout the transaction process.

[0094] In summary, steps 501 to 505 not only effectively verify the logical correlation between geographic block information and voiceprint data, but also significantly enhance the data security and consistency in the real estate registration process by comprehensively applying various advanced technical means such as zero-knowledge proof protocol and secure multi-party computing. The entire process is carefully designed, taking into full account various complex factors in actual application scenarios, and provides strong technical support for areas such as real estate transactions involving sensitive information. In particular, while protecting user privacy, the authenticity and integrity of the data are ensured.

[0095] In order to further improve the data security and privacy protection effect in the process of real estate registration, this solution focuses on the process of generating response generation rules based on the real-time status parameters of the transport carrier, by analyzing the amplitude fluctuation of the dynamic state sequence and the frequency domain offset of the voiceprint confusion factor, combining the curvature radius of the logistics path to generate a dynamic weight factor, and interweaving it with the trajectory and voiceprint fragmentation to form a rule template, thereby enhancing the adaptability and flexibility of the rule template. In some embodiments, the generation of response generation rules based on the real-time status parameters of the transport carrier described in step 503 includes: 601. Divide the real-time speed, acceleration and confined space vibration intensity of the transport carrier into a dynamic state sequence according to timestamps, and extract the state parameter component corresponding to each timestamp; In step 601, the real-time speed, acceleration and confined space vibration intensity of the transport carrier are data sets describing the dynamic behavior of the transport vehicle. The timestamp is used to identify the time point of each data record. The dynamic state sequence is a data sequence composed of these parameters arranged in chronological order, which is used to reflect the state changes of the transport carrier at different time points. The state parameter component refers to the specific value extracted from the dynamic state sequence.

[0096] In the embodiment of the present application, the real-time speed, acceleration and confined space vibration intensity of the transport carrier are first collected, and these parameters are sorted by timestamp to form a dynamic state sequence. Then, the state parameter component corresponding to each timestamp is extracted from the sequence as the basis for subsequent processing. Sensor technology is used here to obtain raw data, and it is organized into a sequence form through a simple sorting algorithm. Finally, a data set that can accurately reflect the dynamic characteristics of the transport carrier is obtained.

[0097] 602. Generate a dynamic weight factor bound to a timestamp based on the amplitude fluctuation of the dynamic state sequence and the frequency domain offset of the voiceprint confusion factor, wherein the generation of the dynamic weight factor is subject to a curvature radius of the logistics path as a constraint condition; In step 602, the dynamic weight factor is a value generated based on the amplitude fluctuation of the dynamic state sequence and the frequency domain offset of the voiceprint confusion factor, which is used to adjust the weight of the data processing rules corresponding to different timestamps. The curvature radius of the logistics path is a key geometric parameter that describes the degree of curvature of the logistics path, and is used as a constraint to ensure the rationality and applicability of the dynamic weight factor. The amplitude fluctuation refers to the degree of value change in the dynamic state sequence, while the frequency domain offset refers to the position movement of the voiceprint confusion factor in the frequency domain.

[0098] In the embodiment of the present application, the amplitude fluctuation of the dynamic state sequence and the frequency domain offset of the voiceprint confusion factor are first calculated. Then, the constraint conditions are set according to the curvature radius of the logistics path, and a dynamic weight factor bound to the timestamp is generated using a specific algorithm. This process uses signal processing technology and mathematical models to ensure that the dynamic weight factor can reflect the actual state of the transport carrier and meet the requirements of the path characteristics. Finally, a set of dynamic weight factors is generated, which provides a basis for the construction of subsequent rule templates.

[0099] 603. Interweave the dynamic weight factor with the trajectory slice and the voiceprint slice corresponding to the timestamp to generate a rule template containing the timestamp constraint condition; In step 603, the rule template is a data structure containing timestamp constraints to guide the formulation of subsequent response generation rules. Parameter interleaving refers to the process of combining dynamic weight factors with trajectory sharding and voiceprint sharding to enhance data security and consistency. Timestamp constraints refer to the criteria for sorting data according to the chronological order of events to ensure the temporal continuity and logical relationship of data.

[0100] In the embodiment of the present application, the dynamic weight factor is first interleaved with the trajectory fragments and voiceprint fragments corresponding to the timestamp, and complex mathematical operations and logical operations are used to achieve effective data fusion. Then, a rule template containing timestamp constraints is generated based on the interleaving results. A variety of encryption algorithms are involved here to ensure that the rule template is not only highly secure, but also guarantees the time sequence consistency and logical relevance of the data. Finally, a rule template that contains both time information and high security is formed, which provides guidance for subsequent data processing.

[0101] 604. Encrypt the rule template in slices through secure multi-party computing, distribute the encrypted rule slices to the registration authority, and generate response generation rules based on multi-party collaborative decryption.

[0102] In step 604, shard encryption is the process of segmenting and encrypting the rule template to protect data privacy. The encrypted rule shard is a fragment of the rule template that has been encrypted, 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 parties to complete the decryption task together without leaking their own data. Through this mechanism, efficient information sharing and verification can be achieved while ensuring data security and privacy.

[0103] In the embodiment of the present application, the rule template is first encrypted in slices, and the security of each slice is ensured by using an advanced encryption algorithm. Then, the encrypted rule slices are distributed to different registration agencies. On this basis, through secure multi-party computing technology, each agency collaborates to complete the decryption task and finally generates the response generation rules. 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.

[0104] Here is a specific example: In a specific real estate transaction case, the seller wants to ensure the consistency and security between the geographic location information and voiceprint data it provides. After completing the previous steps, continue the operation according to the method of steps 601 to 604. First, collect the real-time speed, acceleration and vibration intensity of the transport carrier in the confined space to form a dynamic state sequence. Then, generate a dynamic weight factor based on the dynamic state sequence and the voiceprint confusion factor to generate a rule template. Then, encrypt the rule template in slices and distribute the encrypted rule slices to different registration agencies to generate response generation rules. Doing so not only enhances the security and consistency of the data, but also ensures the transparency and credibility of the entire transaction process.

[0105] In summary, steps 601 to 604 not only effectively solve the consistency problem between the real-time status parameters of the transport carrier and the data processing rules by comprehensively applying a variety of advanced technical means, such as sensor technology, signal processing technology, mathematical modeling and encryption algorithms, but also significantly improve the data security and privacy protection level in the real estate registration process. The entire process is carefully designed, taking into full account the various complex factors in the actual application scenarios, providing strong technical support for areas such as real estate transactions involving sensitive information, and greatly improving information security and user trust. In particular, 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 the transparency and fairness of the information sharing process.

[0106] In order to further improve the security and privacy protection of data transmission in the logistics path, this solution explains in detail the method of synchronously encrypting dynamic voiceprint parameters and spatiotemporal coding sequences to generate encryption keys, including phase fluctuation analysis to extract the phase offset sequence corresponding to the main frequency component, interweaving the spatiotemporal coding sequence to generate a mixed coding block, and performing modular addition operations on the chaotic perturbation vector generated by the hash chain, and finally generating an encryption key that dynamically evolves with the logistics path, thereby improving the security and dynamic adaptability of the key. In some embodiments, the step 102 of synchronously encrypting the dynamic voiceprint parameters with the spatiotemporal nodes of the spatiotemporal coding sequence to generate an encryption key that dynamically evolves with the logistics path includes: 701. Perform phase fluctuation analysis on the dynamic voiceprint parameters, extract the phase offset sequence corresponding to the main frequency component, and quantize the integer sequence matching the time-space node timestamp bit width; In step 701, dynamic voiceprint parameters refer to voiceprint feature data that changes with time and environment. Phase fluctuation analysis is the process of processing these data to extract their phase change characteristics. The main frequency component is the most important 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 matching the time-space node timestamp bit width refers to converting the timestamp into an integer sequence matching the time-space node for subsequent operations.

[0107] In the embodiment of the present application, phase fluctuation analysis is first performed on the dynamic voiceprint parameters to identify and extract the main frequency component and its corresponding phase offset sequence. Then, the corresponding integer sequence is generated according to the timestamp of the space-time node to ensure that the bit width of the sequence matches the space-time node. 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 features and a corresponding timestamp integer sequence.

[0108] 702. Interweave the latitude and longitude coordinates of the space-time coding sequence with the timestamp to generate a hybrid coding block, and alternately flip bits of the hybrid coding block based on the parity of the phase offset sequence to generate an initial key segment; In step 702, the spatiotemporal coding sequence is a data set consisting of latitude and longitude coordinates and a timestamp for identifying a specific geographic location and time. The hybrid coding block is a data structure generated by interleaving the latitude and longitude coordinates in the spatiotemporal coding sequence with the timestamp. The initial key segment is a data segment obtained by performing a bit flip operation on the hybrid coding block, and the flipping method is determined based on the parity of the phase offset sequence.

[0109] In the embodiment of the present application, the longitude and latitude coordinates in the spatiotemporal coding sequence are first interleaved with the timestamp to generate a mixed coding block. Then, logical operations and mathematical transformation techniques are used to perform alternating bit flipping operations on 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 spatiotemporal information and has a certain degree of security. Finally, a preliminary key segment is formed, which provides a basis for subsequent encryption.

[0110] 703. Generate a chaotic disturbance vector based on the hash chain of continuous timestamps of the logistics path, perform a bitwise modular addition operation on the initial key segment and the chaotic disturbance vector, and generate an intermediate key segment with dynamic characteristics of the logistics path; In step 703, the hash chain is a series of continuous values ​​generated based on the hash function, which is used to represent the time sequence relationship of the logistics path. The chaotic perturbation vector is a random number sequence generated based on the hash chain, which is used to increase the complexity of the encryption process. The intermediate key segment is a data segment generated by performing a modular addition operation on the initial key segment and the chaotic perturbation vector, which has the dynamic characteristics of the logistics path.

[0111] In the embodiment of the present application, a hash chain is first generated based on the continuous timestamps of the logistics path, and the hash chain is used to generate a chaotic perturbation vector. Then, the initial key segment and the chaotic perturbation vector are bit-wise modulo addition operations are performed. Here, a method combining cryptography technology and chaos theory is used to generate an intermediate key segment with dynamic characteristics of the logistics path, ensuring that the generated intermediate key segment is difficult to predict or crack. Finally, an intermediate key segment that contains both original spatiotemporal information and high security is obtained.

[0112] 704. Perform multiple rounds of displacement disturbance on the intermediate key segment according to the energy attenuation curve of the voiceprint main frequency to generate an encryption key that dynamically evolves along the logistics path.

[0113] In step 704, the voiceprint main frequency energy attenuation curve describes the trend of the energy of the voiceprint signal on its main frequency component over time. Multi-round displacement perturbation refers to the process of multiple position movement and transformation of 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. After further processing, it will form the final encryption key, which has the characteristic of dynamic evolution along the logistics path.

[0114] In the embodiment of the present application, the main frequency energy decay curve in the dynamic voiceprint parameters is first analyzed, and a multi-round displacement perturbation scheme is designed according to its characteristics. Then, the intermediate key segment is moved and transformed multiple times according to a predetermined scheme. Complex mathematical operations and logical operations are used here to adjust the data structure to ensure that each round of displacement disturbance can significantly change the key content and increase the difficulty of cracking. For example, methods such as cyclic displacement and permutation can be used, combined with the characteristics of different stages of the voiceprint main frequency energy decay curve, and different perturbation strategies can be applied round by round. The final result is an encryption key that contains both original spatiotemporal information and is highly secure, and can be dynamically updated as the logistics path changes.

[0115] Here is a specific example: In a specific real estate transaction case, the seller wants to ensure the security of the geographic location information it provides. After completing the previous steps, continue the operation according to the method of steps 701 to 704. First, perform phase fluctuation analysis on the dynamic voiceprint parameters and quantize the integer sequence with matching bit width of the spatiotemporal node timestamp. Next, interleave the longitude and latitude coordinates of the spatiotemporal coding sequence with the timestamp to generate a hybrid coding block and generate an initial key segment. Then, perform modular addition operation on the initial key segment and the chaotic perturbation vector to generate an intermediate key segment with dynamic characteristics of the logistics path. Finally, generate an encryption key that evolves dynamically with the logistics path. The result of this is to ensure the high confidentiality and security of geographic information throughout the transaction process.

[0116] In summary, steps 701 to 704, by combining dynamic voiceprint parameters, spatiotemporal coding sequences and logistics path characteristics, use advanced digital signal processing, cryptography and chaos theory techniques, which not only effectively solves the problem of synchronous encryption of dynamic voiceprint parameters and spatiotemporal coding sequences, but also significantly improves the security and privacy protection of data transmission in the logistics path. The entire process is carefully designed, taking into full account the various complex factors in actual application scenarios, providing strong technical support for areas such as real estate transactions involving sensitive information, and greatly improving information security and user trust. In particular, while protecting data privacy, the authenticity and integrity of the data are ensured. In addition, by introducing the dynamic characteristics of the logistics path, the security and unpredictability of the encryption key are further enhanced.

[0117] Figure 2 A structural diagram of a real estate registration privacy protection system based on zero-knowledge proof is provided for the embodiment of the present application, such as Figure 2 As shown, the device comprises: The acquisition module 21 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; The encryption module 22 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 along the logistics path; A generation module 23 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; The verification module 24 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.

[0118] Figure 2 The real estate registration privacy protection system based on zero-knowledge proof can be executed Figure 1 The implementation principle and technical effect of the real estate registration privacy protection method based on zero-knowledge proof described in the embodiment shown will not be repeated. The specific way in which each module and unit performs operations in the real estate registration privacy protection system based on zero-knowledge proof in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0119] In one possible design, Figure 2 The real estate registration privacy protection system based on zero-knowledge proof of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0120] The processing component 32 is used for the above Figure 1 The embodiment provides a privacy protection method for real estate registration based on zero-knowledge proof.

[0121] 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 method. Of course, the processing component may 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 to perform the above method.

[0122] The storage component 31 is configured to store various types of data to support operations at 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 disk or optical disk.

[0123] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0124] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0125] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0126] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0127] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a privacy protection method for real estate registration based on zero-knowledge proof.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for 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.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A privacy protection method for real estate registration based on zero-knowledge proof, characterized in that: include: Collect logistics trajectory data through multi-source sensors of the transport carrier, encapsulate the logistics trajectory data into a physical carrier for the transfer of the warrant, and generate a spatiotemporal coding sequence; Capturing dynamic voiceprint parameters based on the acoustic resonance effect of the confined space of the transport carrier, encrypting the dynamic voiceprint parameters synchronously with the spatiotemporal nodes of the spatiotemporal coding sequence, and generating an encryption key that evolves dynamically along the logistics path; Binding the geographic coordinates of the spatiotemporal coding sequence with the voiceprint parameters of the encryption key, and generating a joint fingerprint of the logistics trajectory and the voiceprint feature based on the timestamp constraint condition; The joint fingerprint is split into trajectory fragments and voiceprint fragments, and the logical association verification is performed on the trajectory fragments and the voiceprint fragments through a zero-knowledge proof protocol and secure multi-party computing. The trajectory fragments and the voiceprint fragments are recombined only when the timestamp constraint condition is met to verify the consistency of the real estate registration data, and isolate the complete path of the spatiotemporal coding sequence and the plaintext parameters of the encryption key.

2. The method according to claim 1, characterized in that The method of generating a joint fingerprint of logistics track and voiceprint feature based on the timestamp constraint condition includes: The geographic coordinates of the spatiotemporal coding sequence are divided into discrete geographic blocks according to the continuous timestamps of the logistics path. Each geographic block corresponds to the displacement range of the transport carrier within a fixed time interval. generating a voiceprint confusion factor synchronized with the geographic block based on phase fluctuations of the voiceprint parameters of the encryption key within a corresponding time interval; The longitude and latitude coordinates of the geographic block are overlapped with the voiceprint confusion factor, and the coordinate value of the geographic block, the voiceprint confusion factor and the corresponding timestamp are interleaved based on a chaotic rule to generate a block confusion unit; According to the continuity constraint of the logistics path, the timestamp of the block confusion unit is associated with the voiceprint confusion factor in a hash chain to generate a joint fingerprint of the logistics track and the voiceprint feature.

3. The method according to claim 2, characterized in that The method of overlapping the longitude and latitude coordinates of the geographic block with the voiceprint confusion factor, interleaving the coordinate value of the geographic block, the voiceprint confusion factor and the corresponding timestamp based on a chaotic rule to generate a block confusion unit includes: Decomposing the longitude and latitude coordinates of the geographic block into a longitude sequence and a latitude sequence, and generating a coordinate replacement sequence based on the closed space parameters of the transport carrier; Extract the frequency domain characteristics and time domain period of the voiceprint confusion factor according to the logistics path displacement range corresponding to the timestamp of the time window associated with the geographic block, and generate an interleaving weight vector; The longitude sequence, the latitude sequence and the interleaving weight vector are shifted in order, and the shifted coordinate components and voiceprint confusion factors are iteratively permuted based on the chaotic initial disturbance value to generate a coordinate voiceprint interleaving vector; The coordinate voiceprint interleaved vector is dynamically disturbed according to the moving direction of the transport carrier, and the disturbed coordinate voiceprint interleaved vector is cross-superimposed with the coordinate permutation sequence to generate a block confusion unit.

4. The method according to claim 3, characterized in that The method of iteratively replacing the offset coordinate components and voiceprint confusion factors based on the chaotic initial disturbance value to generate a coordinate voiceprint interleaved vector includes: Based on the confined space resonance frequency of the transport carrier and the displacement range of the logistics path, a dynamic chaos seed associated with the initial chaos disturbance value is generated; Arranging the offset coordinate components in timestamp order as an input vector, and injecting the dynamic chaos seed into the first and last nodes of the input vector to generate a chaos iteration input queue; According to the real-time acceleration and vibration intensity of the transport carrier, the iteration step length of the chaotic mapping is obtained, and the vector elements of the chaotic iteration input queue are cyclically shifted and parameter replaced to generate a chaotic disturbance intermediate vector; The chaotic disturbance intermediate vector is segmented according to the logistics path corresponding to the timestamp, and the longitude sequence, latitude sequence and voiceprint confusion factor are cross-recombined to generate a coordinate voiceprint interleaved vector.

5. The method according to claim 1, characterized in that: The performing of logical correlation verification on the track slice and the voiceprint slice through the zero-knowledge proof protocol and secure multi-party computing includes: Applying the speed change parameter of the transport carrier and the resonance frequency parameter of the enclosed space to the trajectory slice and the voiceprint slice respectively to generate a trajectory challenge vector and a voiceprint challenge vector; Splitting the trajectory challenge vector and the voiceprint challenge vector into verification fragment groups according to the constraints of the timestamp, and distributing the obfuscated trajectory fragments and voiceprint fragments to different registration agencies; generating response generation rules based on the real-time status parameters of the transport carrier, and synchronizing the response generation rules among registration institutions through secure multi-party computing; In the zero-knowledge proof protocol, segmented perturbation is performed on the verification shard group according to the response generation rule to obtain a partial response shard, wherein the partial response shard includes a timestamp continuity identifier; The local response shards are cross-validated, and only when the identifiers satisfy the timestamp continuity and are consistent with the response generation rules, shard reorganization is triggered to verify the consistency of the real estate registration data.

6. The method according to claim 5, characterized in that The generating response generation rule based on the real-time status parameter of the transport carrier comprises: The real-time speed, acceleration and confined space vibration intensity of the transport carrier are divided into a dynamic state sequence according to the timestamp, and the state parameter component corresponding to each timestamp is extracted; Based on the amplitude fluctuation of the dynamic state sequence and the frequency domain offset of the voiceprint confusion factor, a dynamic weight factor bound to the timestamp is generated, wherein the generation of the dynamic weight factor is subject to the curvature radius of the logistics path as a constraint condition; Interweaving the dynamic weight factor with the trajectory slice and the voiceprint slice corresponding to the timestamp to generate a rule template containing the timestamp constraint condition; The rule template is encrypted in slices through secure multi-party computing, the encrypted rule slices are distributed to the registration agency, and response generation rules are generated based on multi-party collaborative decryption.

7. The method according to claim 1, characterized in that The step of synchronously encrypting the dynamic voiceprint parameters with the spatiotemporal nodes of the spatiotemporal coding sequence to generate an encryption key that evolves dynamically along the logistics path includes: Performing phase fluctuation analysis on the dynamic voiceprint parameters, extracting the phase offset sequence corresponding to the main frequency component thereof, and quantizing an integer sequence matching the bit width of the spatiotemporal node timestamp; 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 privacy protection method for real estate registration 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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