Logistics cargo path privacy protection method based on block chain right proof

Abstract

The invention discloses a logistics cargo path privacy protection method based on a right and interest proof of a block chain, and relates to a block chain technology, and the method comprises the following steps: building a block chain network, building a block chain network based on the right and interest proof, determining node roles and permissions in the network, and determining a specific implementation mode of a right and interest proof mechanism; defining a data structure, and designing a data structure for storing logistics cargo path information, including a transaction format, path node information, a timestamp and the like; deploying a smart contract, namely deploying the smart contract in the block chain network for managing storage, verification and query operation of the path data; internet-of-things equipment data acquisition: acquiring path information such as the position and the state of the goods in real time through the Internet-of-things equipment; and data packaging and signing. The method has the advantages that whether the data is encrypted or not is dynamically selected according to the privacy level of the data, the data with the high privacy level is encrypted by using an encryption algorithm, and the security of the data in the transmission and storage process is ensured.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a method for protecting the privacy of logistics cargo paths based on blockchain equity proof. Background Art

[0002] The logistics cargo path query method based on blockchain equity proof currently has the following defects and shortcomings:

[0003] 1. In the logistics cargo route query scenario, how to ensure the authenticity and integrity of logistics data is an important challenge. Since the logistics process involves multiple parties and complex interactions, data may be tampered with or forged during transmission and storage. This may not only lead to distortion of logistics information, but also cause a crisis of trust and business losses. Therefore, it is necessary to find an effective method to ensure the authenticity and integrity of data while protecting the privacy of data and preventing sensitive information from being leaked.

[0004] 2. Different users have different needs for privacy protection. Some users may want to disclose some data in exchange for a better service experience, while some users want to strictly protect the privacy of all data. However, existing privacy protection strategies are often too single and cannot meet the personalized needs of users.

[0005] 3. In logistics cargo path queries, the application of privacy protection technology without a utility evaluation mechanism faces the problem of a trade-off between the degree of privacy protection and data utility, which may lead to data privacy distortion.

[0006] Based on the above problems, the present invention designs a logistics cargo path privacy protection method based on blockchain equity proof. Summary of the invention

[0007] The purpose of the present invention is to solve the problems in the prior art and to propose a logistics cargo path privacy protection method based on blockchain equity proof.

[0008] A method for protecting the privacy of logistics cargo paths based on blockchain equity proof: comprising the following steps:

[0009] 1. Plan the network topology, select a suitable blockchain platform, and set the roles and permissions of the nodes to ensure that each node participates in the network operation according to the rules. Subsequently, we implement the proof of stake mechanism, determine the probability of selecting the verification node by the amount of collateral, use a random algorithm to select the verification node responsible for the verification and addition of new blocks, and set up a reward and punishment mechanism to encourage nodes to participate in good faith;

[0010] 2. In the process of defining the data structure to store the logistics cargo path information, in-depth analysis is conducted based on the characteristics of the logistics path data to ensure that the designed data structure can store and manage this data efficiently and accurately. The path node information contains detailed data such as the node location, timestamp, cargo status, etc. These data are organized and stored through specific data fields for subsequent query and analysis.

[0011] 3. Deploy smart contracts, upload the smart contract code to the chain, make the contract part of the blockchain network, and be able to automatically execute the predetermined functions on the network. After the contract is written, it needs to be rigorously tested and verified to ensure its stability and security in the actual environment. Select a suitable blockchain network for deployment. At the same time, weigh the performance, security, cost and other factors of different blockchain networks. Once the network is selected, you need to prepare the deployment environment, including configuring nodes, setting network parameters, etc. During the deployment process, you need to specify the contract address and initial parameters (if any), and pay a certain handling fee to the blockchain network. After successful deployment, the blockchain network will return the deployment transaction hash and contract address of the contract;

[0012] 4. IoT device data collection is a critical process, which involves collecting the location, status and other path information of the goods in real time through IoT devices. IoT devices, such as sensors, GPS locators, etc., are deployed on the goods or at key locations on the transportation path. These devices can monitor the state changes of the goods, such as temperature, humidity, pressure, etc., as well as the location information of the goods in real time. During the data collection process, the IoT devices send the collected data to a central server or cloud platform through wireless communication technology. After preliminary processing, the data is stored in a standardized format to facilitate subsequent analysis and processing. IoT devices usually use multiple sensors for redundant collection, and use algorithms to verify and correct the collected data to ensure the accuracy of the data. In addition, in order to prevent data loss or tampering, data encryption and signature technologies are also used to ensure the security of data during transmission and storage;

[0013] 5. Data encapsulation and signature. In the process of processing logistics path data, once the path information such as the location and status of the goods is collected through the IoT device, the data needs to be encapsulated and signed. The process of data encapsulation involves converting the collected raw data into a data packet that conforms to the blockchain transaction format. This step includes formatting, encoding and structuring of data to adapt to the transaction standards and requirements of the blockchain network. The data packet will contain necessary meta-information, such as the sender, receiver, timestamp, and specific path information content, to ensure the correct transmission and identification of data in the blockchain network. Next, the application of digital signature technology is crucial to ensure the integrity and immutability of data. The sender will use the private key to digitally sign the encapsulated data packet. This signature is a unique identifier that is closely bound to the sender's identity. When the data packet is transmitted on the blockchain network, other nodes can use the sender's public key to verify the validity of the signature, thereby ensuring that the data packet has not been tampered with during transmission. The use of digital signatures not only enhances the security of data, but also provides an authentication mechanism for the source of data. By verifying the signature, the receiver can confirm the integrity of the data and the identity of the sender, thereby establishing a foundation for data trust. This trust mechanism is particularly important in the processing of logistics path data, because it involves multi-party participation and data sharing, and it is necessary to ensure the authenticity and reliability of the data. In order to implement a flexible privacy protection strategy, the design of the data structure uses encryption and anonymization. For data with a high privacy level, encryption fields can be reserved when defining the data structure, and encryption algorithms can be used for encryption processing during the data processing stage. For data that needs to be anonymized, a corresponding anonymization algorithm can be designed to replace sensitive information with meaningless placeholders or hash values ​​to protect user privacy. At the same time, an adaptive encryption evaluation model is introduced to determine which algorithm to use for dynamic encryption. The model can automatically select the most appropriate encryption algorithm based on factors such as the data's privacy level, security requirements, performance requirements, and computing resources;

[0014] 6. Transaction upload. First, the node with the signed transaction will build a complete transaction containing all necessary information. This includes but is not limited to the transaction ID, sender address, receiver address, transaction data (i.e. the encapsulated and signed logistics path information), and timestamp. This information is organized in a specific format required by the blockchain network to ensure that it can be correctly parsed and processed by the nodes in the network. The node will broadcast the constructed transaction to the blockchain network. Broadcasting is usually carried out through specific communication protocols and ports to ensure that the transaction can be received by other nodes in the network. The key to this step is to ensure the rapid and reliable propagation of transactions so that as many nodes as possible can participate in the transaction verification process. Once the transaction is broadcast to the network, the nodes in the network will begin to verify the transaction. The verification process includes checking whether the transaction format is correct, whether the signature is valid, whether the sender has sufficient authority to conduct the transaction, etc. The node will also check whether the transaction data conforms to the expected logistics path information format to ensure the integrity and accuracy of the data. If the transaction passes the verification, the node will add it to its own transaction pool and wait to be packaged into the block. Over time, consensus participating nodes will select a certain number of transactions, package them into a new block, and link them to the blockchain through consensus mechanism and proof of stake. Once the new block is successfully added to the blockchain, the transaction is considered completed and confirmed by the network. At this point, the logistics route data has been securely stored in the blockchain and can be queried and verified by any node in the network.

[0015] 7. Proof of Stake is a transaction verification process based on the proof of stake mechanism, which ensures that only nodes with sufficient stake can participate in the verification, thereby improving the security and credibility of the blockchain network. The proof of stake mechanism requires that nodes must have a certain amount of cryptocurrency or other forms of proof of stake to prove their stake in the network. These proofs of stake can be regarded as "collateral" for the node to participate in the verification, ensuring that the behavior of the node during the verification process is closely related to the stake it holds. When a node receives a transaction, it verifies the transaction according to the PoS mechanism. During this process, the node checks the validity, legality and stake of the sender. The PoS mechanism does not require nodes to solve complex mathematical problems, but determines the probability of participating in the verification based on the amount of stake held by the node. Nodes with more stake will have a higher probability of being selected to participate in the verification of the transaction. This is because they have more proof of stake and contribute more to the network. Once a node is selected to participate in the verification, it will perform a series of verification steps, including checking the signature of the transaction, the sender's account balance, the compliance of the transaction, etc.

[0016] 8. Data consistency check is a crucial part of blockchain technology. It ensures the integrity and consistency of transaction data, thereby ensuring the authenticity and credibility of the data. In the blockchain application of logistics route data, this step is particularly critical. When the transaction data is uploaded to the blockchain network, the verification node will immediately start the data consistency check process. These nodes will use the distributed characteristics of the blockchain to communicate and collaborate with other nodes to jointly verify the integrity of the transaction data. In the data consistency check, the verification node will execute a series of algorithms and rules to verify the authenticity of the data. These algorithms include but are not limited to cryptographic technologies such as hash functions and digital signatures. The hash function can convert transaction data into a hash value of a fixed length. Any slight change to the data will cause the hash value to change significantly. The digital signature is used to verify the source and integrity of the data and ensure that the data has not been tampered with during transmission. In addition, the verification node will check whether the structure and content of the transaction data conforms to the expected format and rules. If the verification node finds any data that does not conform to the rules or expectations, they will reject the transaction and mark it as invalid. Through this series of data consistency check processes, the verification node can ensure the integrity and consistency of the transaction data.

[0017] 9. Once a transaction passes the stake verification and data consistency check, it will be considered a valid transaction and ready to be added to the blockchain. At this point, nodes in the network will win the right to add a new transaction block based on the stake they hold. The winning node will create a new block and package the verified transactions into this block. Each block contains a certain amount of transaction data, as well as block meta-information such as block height, timestamp, etc. These blocks are linked together in chronological order to form a blockchain.

[0018] 10. Users need to input query requests through the system interface or API interface. When users input requests containing query conditions such as cargo identification and time range through the system interface or API interface, the smart contract will first parse and verify these conditions to ensure the legitimacy of user permissions. Subsequently, the smart contract will deeply analyze the privacy level of the data involved in the request, use the privacy protection mechanism in the blockchain, and use differential privacy encryption to partially return or anonymize data with high privacy levels. During the processing, the smart contract must also ensure the security of the operation, prevent potential attacks, and optimize query performance to improve user experience. In the end, the carefully processed data will be returned to the user according to the predetermined scope and level of detail, which not only meets the user's query needs, but also effectively protects the privacy of the data.

[0019] 11. Request parsing and conversion are key links in the query process. The system will accurately parse the query request entered by the user and convert it into a query instruction that the system can recognize. This step ensures the accuracy and efficiency of the query and lays a solid foundation for subsequent data retrieval and display.

[0020] 12. Smart contract execution is the core link of the query process. When the query request is parsed, the smart contract is automatically triggered to execute. According to the set query conditions, the relevant path information is accurately retrieved in the blockchain network to ensure the accuracy and real-time nature of the data and provide users with reliable logistics information query services.

[0021] 13. Smart contract execution is the core link in the process of logistics route data query. It realizes automated query operation and greatly improves query efficiency and accuracy. After the user's query request is parsed and converted, the system will automatically trigger the corresponding smart contract. These smart contracts have built-in query logic and algorithms, which can accurately retrieve relevant route information in the blockchain network according to the query conditions set by the user. In the design of smart contracts, the choice of data structure is crucial.

[0022] 14. Privacy protection mechanism plays a vital role in the process of logistics route data query, especially through the use of differential privacy protection technology. The core of differential privacy protection technology is to add noise to the original data so that the processed data is still valid in statistical analysis, but cannot locate specific individuals or records. During the query process, the system will first process the query request entered by the user to ensure that it meets the requirements of differential privacy protection. Then, the system will add an appropriate amount of noise to the original data set according to the differential privacy algorithm to generate a "fuzzy" data set. This fuzzy data set retains the statistical characteristics of the original data without revealing specific personal or record information. When the user submits a query request, the system will search in the fuzzy data set and return the result to the user. Since the result data is processed by differential privacy, even if the attacker obtains the data, it cannot restore the original sensitive information from it. This realizes privacy protection in the user query process and effectively prevents the leakage of sensitive information. In the query of logistics cargo routes, the application of differential privacy protection technology faces the trade-off between the degree of privacy protection and data utility. This is a complex and critical decision-making process that requires comprehensive consideration of many factors, such as user privacy needs, data usage purposes, and technical implementation costs. Therefore, we introduced an evaluation model to ensure that data utility is not lost while ensuring privacy protection. At the same time, we optimized the noise generation algorithm for differential privacy protection, making the noise generation distribution more reasonable and improving the utility of the data. Through this mechanism, we can ensure that personal privacy is fully protected while enjoying convenient query services.

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] 1: Utilize the decentralized and tamper-proof characteristics of blockchain to build a logistics information blockchain based on proof of stake. Each logistics node needs to be verified by the proof of stake mechanism when uploading data to ensure the authenticity and integrity of the data. Through smart contracts, logistics information can be automatically recorded, verified and stored, reducing the possibility of human intervention and tampering;

[0025] 2: Introduce adaptive privacy encryption strategies to meet the privacy needs of different users, and design multi-level privacy encryption schemes based on user privacy preferences and service requirements. For users who want to disclose part of their data, provide partial encryption services based on attribute encryption or differential privacy;

[0026] 3: The improved differential privacy algorithm has a certain improvement in the rationality of noise generation, which can avoid the problem of data distortion caused by excessive noise. By introducing a privacy evaluation model to evaluate the performance of differential privacy protection, it can help us quantify the effect of privacy protection and the availability of data; BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a structural schematic diagram of the first perspective of a logistics cargo path privacy protection method based on blockchain equity proof proposed by the present invention; DETAILED DESCRIPTION

[0028] Reference Figure 1 , combined with the accompanying drawings, the method of using the present invention is as follows:

[0029] 1. Establish a blockchain network, build a blockchain network based on proof of stake, determine the node roles and permissions in the network, and the specific implementation method of the proof of stake mechanism;

[0030] 2. Define the data structure and design the data structure used to store logistics cargo path information, including transaction format, path node information, timestamp, etc.;

[0031] 3. Deploy smart contracts. Deploy smart contracts in the blockchain network to manage the storage, verification and query operations of path data;

[0032] 4. IoT device data collection: collect the location, status and other path information of goods in real time through IoT devices;

[0033] 5. Data encapsulation and signature: encapsulate the collected data into a data packet that conforms to the blockchain transaction format, and use digital signature technology to ensure the integrity and immutability of the data. Dynamically choose whether to encrypt the data based on the privacy level of the data. For data with a high privacy level, use encryption algorithms to encrypt the data to ensure the security of the data during transmission and storage;

[0034] 1) An adaptive encryption evaluation model is introduced to determine which algorithm to use for dynamic encryption. The model can automatically select the most appropriate encryption algorithm based on factors such as the privacy level, security requirements, performance requirements, and computing resources of the data. The adaptive encryption evaluation algorithm model is as follows:

[0035] First, we define the following variables:

[0036] D: Data characteristics (e.g., data sensitivity, size, distribution, etc.);

[0037] P: privacy requirements (e.g., privacy budget, anonymization level, etc.);

[0038] C: performance requirements (e.g., processing speed, response time, etc.);

[0039] R: computing resources (e.g., available memory, number of CPU cores, etc.);

[0040] We can then assign a weight to each variable that reflects their importance in determining the encryption algorithm. The weights can be set based on actual application scenarios and expert opinions. Assume that we assign a weight value wD, wP, wC, and wR to each variable, and the sum of these weights is 1.

[0041] Next, we can define a scoring function S(A) for each encryption algorithm, where A is the encryption algorithm. This function will take into account the above variables and weights and return a score representing the suitability of the algorithm. The specific form of the scoring function can be designed as needed, but the following is a simple example:

[0042] S(A)=wD·D(A)+wP·P(A)+wC·C(A)+wR·R(A)

[0043] in:

[0044] D(A): The adaptability score of algorithm A to data feature D;

[0045] P(A): the satisfaction score of algorithm A for privacy requirement P;

[0046] C(A): the satisfaction score of algorithm A for performance requirement C;

[0047] R(A): the utilization score of algorithm A on computing resource R;

[0048] Each scoring function D(A), P(A), C(A), and R(A) can be defined based on specific algorithms and data characteristics. For example, for the data characteristic score D(A), we can consider the efficiency of the algorithm in processing specific data types or data distributions; for the privacy requirement score P(A), we can consider the anonymization level or encryption strength provided by the algorithm; for the performance requirement score C(A), we can consider the execution time or throughput of the algorithm; for the computing resource score R(A), we can consider the memory usage or CPU utilization of the algorithm.

[0049] 6. Deploy smart contracts and upload smart contract codes to the blockchain, so that the contracts become part of the blockchain network and can automatically execute predetermined functions on the network. In order to improve the privacy protection of smart contract execution, we have desensitized and encrypted the data, introduced an improved differential privacy encryption algorithm, and encrypted key data to ensure that it will not be obtained by unauthorized third parties during data processing.

[0050] 7. Transaction upload: upload the signed transaction to the blockchain network, which will be verified and stored by the nodes in the network;

[0051] 8. Stake verification: nodes verify received transactions based on the stake proof mechanism, ensuring that only nodes with sufficient stake can participate in the verification process;

[0052] 9. Data consistency check: Verification nodes check the integrity and consistency of transaction data to ensure that the data has not been tampered with. In addition to checking the integrity and consistency of the data, they can also check whether the privacy protection level of the data is correctly set and executed;

[0053] 10. Data storage: Verified transactions are added to the blockchain to form an immutable data record. Depending on the privacy level of the data, the data is stored in different blocks or locations so that different levels of access control can be performed as needed later.

[0054] 11. The user enters a query request through the system interface or API interface, including query conditions such as cargo identification and time range. The smart contract can determine the scope and level of detail of the data returned to the user based on the user's query request and the privacy level of the data. For data with a high privacy level, only partial information or anonymized data can be returned;

[0055] 12. Request parsing and conversion: the system parses and converts the query request input by the user and generates executable query instructions;

[0056] 13. Smart contract execution, triggering the corresponding smart contract to execute the query operation, and retrieve the relevant path information in the blockchain network according to the query conditions;

[0057] 14. Encrypt the query result data to ensure data security. In addition to conventional data encryption processing, differential privacy technology is combined, and the privacy utility evaluation in differential privacy protection is introduced and the noise distribution mechanism is optimized to improve the effectiveness and efficiency of privacy protection to a certain extent; the improved differential privacy algorithm has certain improvements in the generation algorithm and noise effect evaluation, which can avoid data distortion caused by excessive noise and excessive consumption of computing resources.

[0058] By introducing some algorithms, the distribution of noise is made more reasonable to avoid data distortion caused by over-dense or over-sparse data, and privacy is protected by adding Laplace distribution noise to the query results. The probability density function of Laplace distribution is:

[0059] f(x|μ,b)=1 / (2b)*exp(-|x-μ| / b)

[0060] Where μ is the location parameter and b is the scale parameter. In the context of differential privacy, μ is usually set to 0, while b is related to the sensitivity of the query and the privacy budget ε.

[0061] Sensitivity and global sensitivity,Sensitivity is an indicator to measure the sensitivity of the query function to changes in the input data set. The global sensitivity Δf is defined as:

[0062] Δf=max||f(D1)-f(D2)||1

[0063] Where D1 and D2 are any two adjacent datasets (i.e., datasets with only one element different), and ||·||1 represents the L1 norm. The global sensitivity reflects the maximum output difference of the query function over all possible dataset variations.

[0064] Privacy budget and noise amount,Privacy budget ε is a key parameter in differential privacy, which controls the degree of privacy protection and the amount of noise.,In the Laplace mechanism, the relationship between the noise scale parameter b,,privacy budget ε and global sensitivity Δf is: b = Δf / ε, the smaller the privacy budget ε,,the higher the degree of privacy protection, and the greater the amount of noise,needed to be added.

[0065] Noise Generation Algorithm,Based on the above formula, the noise generation algorithm can be described as,follows:

[0066] Calculate the global sensitivity Δf of the query function, set the privacy budget ε according to the privacy requirement, calculate the scale parameter b of the Laplace distribution according to b = Δf / ε, and randomly extract noise values ​​from the Laplace distribution (μ = 0, b = Δf / ε). Add the noise value to the query result to obtain the output of differential privacy protection.

[0067] The optimal design of noise mechanism in differential privacy is a complex and critical task, which involves knowledge from multiple fields such as mathematics, statistics, and computer science.

[0068] By introducing a privacy evaluation model to evaluate the performance of differential privacy protection, it can help us quantify the effectiveness of privacy protection and the availability of data. The following are some algorithmic formulas that support the design of this evaluation model.

[0069] 1) Privacy protection metrics,The performance evaluation of differential privacy usually includes metrics such as privacy loss, privacy budget, and global sensitivity.

[0070] Privacy budget (ε): Privacy budget is a key parameter in differential privacy, which measures the strength of privacy protection. The smaller the privacy budget, the higher the privacy protection.

[0071] Privacy loss: Privacy loss can be used to quantify the risk of privacy leakage during a specific query or data release process.

[0072] 2) Data utility metrics

[0073] In addition to privacy protection, it is also necessary to evaluate the utility of the data, that is, to what extent the data after differential privacy processing retains its original usefulness.

[0074] Data utility loss: This can be measured by comparing the performance difference between the original data and the data processed with differential privacy.

[0075] 3) Performance evaluation model design

[0076] Privacy protection effect evaluation: Use the privacy budget ε to quantify the privacy protection strength.

[0077] Privacy loss can be measured using the following formula:

[0078] (PrivacyLoss=f(ε,dataset,query))

[0079] where (f) is a function that depends on the privacy budget ε, the dataset, and the query.

[0080] Data utility evaluation: Data utility loss can be measured using the performance difference between original data and differentially private processed data on a specific task.

[0081] In classification tasks, metrics such as precision, recall, or F1 score can be used to evaluate data utility.

[0082] 4) Comprehensive evaluation

[0083] Taking privacy protection effect and data utility into consideration, a comprehensive evaluation index can be designed;

[0084] (OverallScore=α×PrivacyScore+β×UtilityScore)

[0085] Among them, (α) and (β) are weight coefficients used to balance the importance between privacy protection and data utility.

[0086] When designing a model, factors such as the characteristics of the data, privacy protection requirements, and computing resources need to be considered.

[0087] The parameters and weights of the model are determined through experiments or domain knowledge.

[0088] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. A method for protecting the privacy of logistics cargo paths based on blockchain equity proof, characterized in that: The following steps are involved: S1: Establish a blockchain network, build a blockchain network based on proof of stake, determine the node roles and permissions in the network, and the specific implementation method of the proof of stake mechanism; S2: Define the data structure and design the data structure used to store logistics cargo path information, including transaction format, path node information, timestamp, etc.; S3: Deploy smart contracts in the blockchain network to manage the storage, verification and query operations of path data; S4: IoT device data collection, which collects the location, status and other path information of goods in real time through IoT devices; S5: Data encapsulation and signature: encapsulate the collected data into a data packet that conforms to the blockchain transaction format, and use digital signature technology to ensure the integrity and immutability of the data; S6: Transaction upload: upload the signed transaction to the blockchain network, which will be verified and stored by the nodes in the network; S7: Stake verification, nodes verify received transactions according to the proof-of-stake mechanism, ensuring that only nodes with sufficient stake can participate in the verification process; S8: Data consistency check: the verification node checks the integrity and consistency of the transaction data to ensure that the data has not been tampered with. In addition to checking the integrity and consistency of the data, it can also check whether the privacy protection level of the data is correctly set and executed; S9: Data storage. Verified transactions are added to the blockchain to form an immutable data record. Depending on the privacy level of the data, the data is stored in different blocks or locations for subsequent access control at different levels. S10: The user inputs a query request. The user inputs a query request through the system interface or API interface, including query conditions such as cargo identification and time range. The scope and detail of the data returned to the user are determined based on the user's query request and the privacy level of the data. For data with a high privacy level, only partial information or anonymized data is returned; S11: Request parsing and conversion: the system parses and converts the query request input by the user to generate an executable query instruction; S12: Smart contract execution, triggering the smart contract to execute a query operation and retrieve relevant path information in the blockchain network according to the query conditions; S13: Encrypt the query result data to ensure data security.

2. According to claim 1, a method for protecting the privacy of logistics cargo paths based on blockchain equity proof is characterized in that: In the process of querying logistics path data, differential privacy protection technology is adopted. During the query process, the system will first process the query request input by the user to ensure that it meets the requirements of differential privacy protection. Then, the system will add an appropriate amount of noise to the original data set according to the differential privacy algorithm to generate a "fuzzy" data set. This fuzzy data set retains the statistical characteristics of the original data and cannot reveal specific personal or record information. When the user submits a query request, the system will search in the fuzzy data set and return the result to the user. Since the result data is processed with differential privacy, even if the attacker obtains the data, the original sensitive information cannot be restored from it, thus realizing privacy protection in the user query process and effectively preventing the leakage of sensitive information.

3. According to a method for protecting the privacy of logistics cargo paths based on blockchain equity proof according to claim 1, it is characterized in that: During the data encapsulation and signing process, we dynamically choose whether to encrypt the data based on the privacy level of the data. For data with a high privacy level, we use encryption algorithms to encrypt the data to ensure the security of the data during transmission and storage.

4. According to claim 1, a method for protecting the privacy of logistics cargo paths based on blockchain equity proof, characterized in that: In the logistics cargo path query, based on user privacy requirements, data usage purposes, technical implementation costs and other purposes, the privacy utility evaluation in differential privacy protection is introduced and the noise distribution mechanism is optimized.

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