Intelligent logistics authorization cloud-assisted PSI-CA method supporting block chain and electronic equipment

Abstract

The invention discloses an intelligent logistics authorization cloud-assisted PSI-CA method supporting a block chain and electronic equipment. The method comprises the following steps: outputting a public parameter by a BC; an accounting node of the BC outputs parameters required by each participant, and the DO, the C1 and the C2 set respective signature keys and publish public keys; the DU submits vouchers alpha and beta to a system consensus node for registration, generates a signature verification key pair and issues a public key; the DO and the DU respectively select random numbers as blind factors, generate confusion ciphertexts A, A ', T and Q, sign the confusion ciphertexts A, A', T and Q, package data according to classes, and respectively send the data to C1 and C2; c1 and C2 take the packed data of the DO and the DU and the public keys of the corresponding users as input and output Boolean values; when all the results are 1, the C1 and the C2 generate confusion ciphertexts U and W; c1 and C2 carry out authorization verification on the DU, after verification is passed, confused ciphertexts U and W are sent to the DU, and the DU verifies the data after receiving the data; and after all verification is passed, the DU performs statistical information analysis based on the received data. According to the method, the security of the user data can be improved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method and an electronic device. Background Art

[0002] In recent years, the e-commerce and logistics industries have grown significantly. Logistics-related companies are in urgent need of advanced data sharing technologies to achieve modernization and intelligent transformation, thereby improving business efficiency and service quality and meeting rapidly changing market demands. However, in the context of the rapid development of the digital economy, data leakage has become a frequent security incident worldwide. The logistics data in the logistics Internet of Things involves a large amount of sensitive information, such as customer privacy, pricing strategies, inventory levels, and operational information. In the logistics Internet of Things, companies face many situations where they need to quantify the degree of data overlap, but simply obtaining statistical information is sufficient. Therefore, how to maximize the value of this information while protecting privacy is an urgent problem to be solved in the current logistics industry.

[0003] The Private Set Intersection Cardinality (PSI-CA) technology provides a solution. It means that the computing participants only collaborate to calculate the number of elements in the intersection of the sets, and no information related to the respective sets is leaked during the calculation process (except the intersection cardinality). This technology can effectively prevent data content leakage and ensure that all parties can maintain the confidentiality of the data while achieving data sharing. However, in logistics data sharing, the traditional private set intersection cardinality still has many limitations:

[0004] (1) Computational and storage pressure. Although the privacy set intersection cardinality-technique has lower computational and storage overhead than other data processing methods, logistics data is often large and complex. Especially in such scenarios with large data volumes and many participants, traditional privacy combined with intersection-cardinality technology places high demands on the local computing power and storage resources of enterprises, which can easily lead to bottlenecks of insufficient computing power and is difficult to meet the needs of efficient data processing.

[0005] (2) User rights protection. Access control mechanisms can ensure that only authorized entities can access user data, reduce the risk of data leakage and privacy infringement, and protect user privacy. However, related technologies fail to provide strict authorization verification, resulting in unauthorized entities being able to participate in data calculations, which damages the legitimate rights and interests of legitimate users and undermines the security and transparency of the collaborative environment.

[0006] (3) Confidentiality and integrity of data. The data transmission process between the participants and the server may face the risk of channel eavesdropping or tampering, especially when it involves sensitive privacy data. In addition, the external server is semi-trusted and may make calculation errors or forge data, affecting the credibility of the calculation results. At present, the privacy set intersection cardinality technology in related technologies fails to monitor the external server and verify the correctness and integrity of the transmission results, which poses a huge threat to the security sharing of enterprises.

[0007] (4) Data abuse. Authorized malicious users may expose private data to others for profit. The current privacy set intersection-cardinality technology does not have a built-in audit mechanism, making it difficult to effectively monitor and record data usage, making it difficult to trace the source of the problem when data is leaked or abused, increasing security and legal risks. Summary of the invention

[0008] The present invention aims to solve one of the technical problems in the related art at least to a certain extent. To this end, the purpose of the present invention is to propose a blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method and electronic device to improve the security of user data.

[0009] To achieve the above-mentioned purpose, the first embodiment of the present invention proposes a blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method, including multiple entities: data owner DO, data user DU, blockchain BC, a first cloud server C1 and a second cloud server C2; the method comprises: step 1, the consensus node of BC takes the security parameter λ as input and outputs the public parameter PP; the accounting node of BC takes the public parameter PP and the parameter r uploaded by DO as input and outputs the parameter P required by each participant DO ,P DU ,pp,γ,DO,C1 and C2 set their respective signature keys (pk B ,sk B ), and and publish the public key; Step 2, DU submits the credentials α and β to the system consensus node for registration. After successful registration, it obtains the required parameters from the chain, generates a signature verification key pair and publishes the public key; Step 3, DO and DU respectively select random numbers as blind factors, and use the public parameter PP and the blind factor to encrypt data, generate obfuscated ciphertext A, A′, T, Q, sign the obfuscated ciphertext A, A′, T, Q, and package the data by class and send them to C1 and C2 respectively; Step 4, C1 and C2 send the packaged data (A, a, σ A ), (A′, a, σ′ A ), (T, σ B ), (Q, σ′ B) and the public key pk of the corresponding user as input, and output a Boolean value of 1 or 0; when all results are 1, C1, C2 respectively use the packaged data sent by DO and DU and the permutation matrix M1, M2 as input to generate obfuscated ciphertext U, W; step 5, C1, C2 perform authorization verification on DU, and after the verification, send the obfuscated ciphertext U, W to DU, and DU performs signature verification and zero-knowledge proof verification on it after receiving the data; step 6, after all the verifications in step 5 are passed, DU performs statistical information analysis based on the received data; step 7, when the authorization verification is successful, take the identity information of DU, tracking key k, and data summary H(Y) as input, output access record AR, and then DO signs it; step 8, when security incidents such as data leakage occur, applicants who meet the application conditions initiate audit requests to trace access records for auditing.

[0010] In addition, the blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method of the embodiment of the present invention may also have the following additional technical features:

[0011] According to an embodiment of the present invention, the specific process of step 1 is as follows: Step 1.1, overall initialization: the consensus node of BC generates public parameters PP = {e, p, g1, g2, G1, G2, G T ,G,H,H1,H2,λ}, define a bilinear mapping e:G1×G2→G T , where G1, G2 and G T is a group of order p, g1 and g2 are generators of groups G1 and G2, and three collision-resistant hash functions are defined: H: {0,1} * →{0,1} λ , H1: {0,1} * →G, H2: G→{0,1} λ , where G is the elliptic curve group, λ is the security parameter, which represents the fixed length of the binary string and is used to output the final pseudo-random value; Step 1.2, node initialization: Different accounting nodes on BC are responsible for different parameter generation and distribution. First, generate the parameters of DU Next, generate the parameters of DO And the parameters required for zero-knowledge proof pp = Setup (1 λ ,C), track the key k, and finally generate the key γ of the random pseudo-random function for identity authentication; Step 1.3, DO initialization: select a random number Upload it to BC to generate parameter P DO Next, set the DO's signature key pair (pk B ,sk B ), finally, generate the data summary H(Y)=(H(y1),H(y2),...,H(yn )); Step 1.4, C1, C2 initialization: C1 and C2 generate corresponding signature key pairs and Used to digitally sign data.

[0012] According to an embodiment of the present invention, the specific process of step 2 is as follows: DU randomly selects authorization parameters α, β as its identity credentials to register with the system, and obtains the required parameters from the chain after successful registration; generates the corresponding signature key pair (pk A ,sk A ) and publish the public key pk A .

[0013] According to an embodiment of the present invention, the specific process of step 3 is as follows: Step 3.1, DU performs preprocessing: DU first preprocesses its private data and selects a random number The ciphertext is calculated as follows:

[0014]

[0015] Let A′=(A -1 ,A -2 ,...,A -m ), then calculate a=H1(α) β , for ciphertext A and A -i Generate signatures σ respectively A =Sign(sk A ,A),σ′ A =Sign(sk A ,A′), finally, DU will (A,a,σ A ) is sent to C1, and (A′,a,σ′ A ) is sent to C2; Step 3.2, DO performs data preprocessing: DO selects a random number The ciphertext is calculated as follows:

[0016]

[0017] Next, let T = (T1, T2, ..., T n ),Q=(Q1,Q2,...,Q n ), generate signatures σ for T and Q respectively B =Sign(sk B ,T),σ′ B =Sign(sk B ,Q). Finally, DO will (T,σ B ), (Q, σ′ B ) are sent to C1 and C2 respectively.

[0018] According to an embodiment of the present invention, the specific process of step 4 is as follows: Step 4.1, signature verification: After C1 and C2 receive data from DO and DU, they perform signature verification. C1 calculates SVerify(pk A ,A,a,σ A ),SVerify(pk B ,T,σ B ); C2 calculates SVerify(pk A ,A′,a,σ′ A ),SVerify(pk B ,Q,σ′ B ), when all results are 1, the verification is successful, otherwise the verification fails. After the verification is successful, C1 and C2 execute the entrusted computing task, otherwise it terminates; Step 4.2, execute the entrusted computing task: C1 Calculation U j =H(e(T j ,A)), and multiply the result with the permutation matrix M1 to obtain U=(U1,U2,...,U n ),for C2 calculates W ij =H(e(Q j ,A -i )), and multiply the result by the permutation matrix M2 to obtain W = (W 11 ,W 12 ,...,W mn ); Step 4.3, generate zk-SNARKs proof: After completing the entrusted computing task, the cloud server needs to prove the computing operations and calculation results. C1 and C2 respectively call the zero-knowledge concise non-interactive knowledge proof generation algorithm to generate proofs π1=Prove(pp,(T,A),U,ω1),π2=Prove(pp,(Q,A′),W,ω2); Step 4.4, sign the data: C1 and C2 summarize all the corresponding data in parallel, package and sign them, and generate a signature

[0019] It should be noted that zk-SNARKs is an effective zero-knowledge proof tool that allows the verifier to prove the authenticity of a statement without disclosing any additional information.

[0020] According to an embodiment of the present invention, the specific process of step 5 is as follows: Step 5.1, authorization verification: the cloud server authenticates the identity of the DU. If the user is an authorized user, it is allowed to access the data, otherwise the data use application is rejected, as follows: C1, C2 calculate the verification information v = F DH-OPRF (α) and token b = a γ, and then send the token b to DU; DU uses the token b issued by the cloud server to randomize the corresponding account information into a verification code v', that is, calculate Then v′ is sent to cloud servers C1 and C2 respectively; finally, C1 and C2 verify whether the verification code v′ of DU is consistent with its corresponding verification information v. If v=v′, C1 will package the data Send to DU, C2 will package the data Send to DU. If v≠v′, the protocol terminates. Step 5.2: Data correctness and integrity verification: After DU receives the packaged data from the cloud server, it performs operation verification and signature verification. If the verification passes, it continues the calculation. The details are as follows: DU uses the public key of the cloud server calculate and To perform signature verification, if the results are all 1, the verification is successful and the calculation continues. After the signature verification is successful, DU verifies the zk-SNARKs proof of the cloud server and calculates PVerify(pp,(T,A),U,π1) and PVerify(pp,(Q,A′),W,π2) respectively. If the results are all 1, the verification is successful and the user accepts the result returned by the cloud server.

[0021] According to an embodiment of the present invention, the specific process of step 6 is: DU performs intersection cardinality calculation based on the received data to obtain the required statistical information, and finally, DU outputs the following information:

[0022]

[0023] According to an embodiment of the present invention, the specific process of step 7 is: after the authorization verification is successful, the identity information of the DU is encrypted and an access record is generated and stored off-chain, specifically as follows: the identity information of the DU is encrypted by the AES encryption mechanism to obtain the ciphertext ID=Enc(k,ID), where k is the key, and the access record is defined as: AR=(ID,H(Y)); then, the DO needs to sign the access record to obtain the signature σ=Sign(sk A ,AR); finally, DU uploads the data summary H(Y) to the blockchain, stores (AR, σ) off-chain, and implements a rotation mechanism for the logs. During the rotation, each access record is guaranteed to contain the encrypted hash value of the previous log to ensure the integrity of the log chain. The old logs are archived to cold storage to reduce the access frequency.

[0024] According to an embodiment of the present invention, the specific process of step 8 is as follows: Application conditions: DO or DU that passes authorization verification and correctly submits data summary; the qualified requester applies to obtain access logs, triggering the audit process: DO first performs signature verification, calculates SVerify(pk A,AR,σ), if the result is 1, the verification is passed. After the verification is passed, the ciphertext is decrypted to obtain id=Dec(k,ID), and finally the access log plaintext is provided for review; otherwise, an additional file damage warning will be returned.

[0025] To achieve the above-mentioned objectives, the second aspect of the present invention proposes an electronic device, comprising a memory, a processor and a computer program stored on the memory. When the computer program is executed by the processor, the above-mentioned blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method is implemented.

[0026] Beneficial effects of the present invention: 1. With the computing power and scalability of the cloud server, the present application can not only process a large amount of data, but also reduce the IT expenses of the enterprise, and enhance the flexibility and response speed of the system under dynamic market demands. 2. The present application introduces an access control mechanism to ensure that only authorized users can participate in privacy set operations, prevent access by unauthorized entities, and enhance data privacy and user trust. 3. The present application includes data encryption, outsourcing verification and transmission verification measures to ensure that the results returned by the cloud service are accurate and reliable, avoid the risks of data eavesdropping and tampering during transmission, and further ensure data security. 4. The present application provides a complete audit tracking mechanism to monitor data access records and computing operations, help users trace the source when data is leaked or misused, and enhance the transparency and security of data use. 5. The present application entrusts complex bilinear pairing calculations to the cloud server. The client only needs to complete some lightweight computing tasks, which is more suitable for practical applications in smart logistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of a blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method according to an embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of entity interaction according to an embodiment of the present invention;

[0029] FIG3( a ) is a schematic diagram showing a comparison of the computational overhead of DO according to an embodiment of the present invention;

[0030] FIG3( b ) is a schematic diagram showing a comparison of the computational overhead of a DU according to an embodiment of the present invention;

[0031] FIG3( c ) is a schematic diagram showing a comparison of total computational overheads according to an embodiment of the present invention;

[0032] FIG4( a ) is a schematic diagram showing a comparison of communication overheads of DO according to an embodiment of the present invention;

[0033] FIG4( b ) is a schematic diagram showing a comparison of communication overheads of DUs according to an embodiment of the present invention;

[0034] FIG4( c ) is a schematic diagram showing a comparison of total communication overhead according to an embodiment of the present invention;

[0035] FIG5( a ) is a schematic diagram showing a comparison of transaction throughputs according to an embodiment of the present invention;

[0036] FIG5( b ) is a schematic diagram showing a comparison of CPU utilization and storage usage according to an embodiment of the present invention;

[0037] FIG. 5( c ) is a schematic diagram showing a comparison of memory usage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0039] Different logistics companies and related service providers can optimize operations and improve service quality by sharing logistics data. However, logistics data contains a lot of private information. Once leaked, companies will face serious consequences such as legal proceedings, damaged brand image, and customer loss. How to ensure data security in data sharing and timely response to security incidents has always been an important issue in intelligent logistics systems.

[0040] The following describes the blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method and electronic device of an embodiment of the present invention with reference to the accompanying drawings.

[0041] Figure 1 It is a flow chart of a cloud-assisted PSI-CA method for intelligent logistics authorization supporting blockchain according to an embodiment of the present invention.

[0042] It should be noted that the blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method of this embodiment includes multiple types of entities: data owner DO, data user DU, blockchain BC, the first cloud server C1 and the second cloud server C2.

[0043] Specifically, Figure 2As shown in the figure, data owners are logistics companies or related service providers, such as suppliers, manufacturers, distributors, and retailers. These companies have collected a large amount of private information through the logistics Internet of Things. However, the data silos between institutions limit the possibility of joint analysis of these data. When the company is the data owner, the system uses an invisible pseudo-random function (OPRF) to strictly control access to data users, ensuring that sensitive information is only accessed by authorized units or personnel, thereby ensuring the security of corporate information. DO is responsible for generating data summaries to facilitate audits. At the same time, DO digitally signs access records to ensure the security of access record storage, thereby ensuring the data security of the company.

[0044] Data users: logistics companies or related service providers, such as suppliers, manufacturers, distributors and retailers. Through this application, each company can integrate data without exposing specific data, thereby optimizing operations. When you need to access the statistical data of each institution, you first need to submit the credentials (α, β) to the consensus node to register with the system and obtain the required parameters; after successful registration, submit the verification code v' generated by the token b and the corresponding user credentials (α, β) to the cloud servers C1 and C2; after identity authentication, DU will obtain the calculation results returned by the cloud server, and can further calculate the required statistical information according to its own needs. In this process, each company will not see any plaintext messages except its own information, which effectively guarantees the data security of other companies. When security incidents such as data leakage occur, companies related to dangerous data can initiate tracking applications to the system. After passing the application conditions, the system can provide access logs for auditing.

[0045] Blockchain: Build a blockchain network between the parties involved in the agreement, using a permissioned chain designed for enterprise-level applications: Hyperledger Fabric. In this application, it will serve as an infrastructure that provides computing power support and security. Use Go language to write smart contracts to generate initialization parameters and process tracking applications, and deploy them on the network nodes of Hyperledger Fabric. Use InterPlanetary File System (IPFS) to manage access records and implement audit tracking of data use. The transparency and immutability of blockchain ensure the fairness of the parameter generation process and the credibility of the audit results. The decentralized nature avoids dependence on a single perishable third party, enhances the reliability of data processing and improves the robustness of the system. Through the automated execution of smart contracts, the system can achieve efficient functional implementation and improve enterprise operational efficiency.

[0046] Cloud server: It is assumed that C1 and C2 are malicious and may make calculation errors, leak data, or tamper with data. In this application, the data uploaded to the cloud server is encrypted and obfuscated, which effectively avoids the security risks of exposing data directly to the cloud server. The cloud server has powerful computing power and abundant storage resources. It is mainly responsible for performing complex bilinear pairing calculations, which significantly improves the operational efficiency of enterprises.

[0047] like Figure 1 As shown, the blockchain-enabled smart logistics authorization cloud-assisted PSI-CA method includes:

[0048] Step 1: The consensus node of BC takes the security parameter λ as input and outputs the public parameter PP; the accounting node of BC takes the public parameter PP and the parameter r uploaded by DO as input and outputs the parameter P required by each participant. DO ,P DU ,pp,γ,DO,C1 and C2 set their respective signature keys (pk B ,sk B ), and And publish the public key.

[0049] Specifically, the specific process of step 1 is:

[0050] Step 1.1, overall initialization: BC consensus nodes generate public parameters PP = {e, p, g1, g2, G1, G2, G T ,G,H,H1,H2,λ}, define a bilinear mapping e:G1×G2→G T , where G1, G2 and G T is a group of order p, g1 and g2 are generators of groups G1 and G2, and three collision-resistant hash functions are defined: H: {0,1} * →{0,1} λ , H1: {0,1} * →G, H2: G→{0,1} λ , where G is the elliptic curve group and λ is the security parameter, which represents the fixed length of the binary string and is used to output the final pseudo-random value.

[0051] Step 1.2, Node Initialization: Different accounting nodes on BC are responsible for different parameter generation and distribution. First, generate the parameters of DU Next, generate the parameters of DO And the parameters required for zero-knowledge proof pp = Setup (1 λ ,C), track the key k, and finally generate the key γ of the invisible pseudo-random function for identity authentication.

[0052] Step 1.3, DO initialization: choose a random number Upload it to BC to generate parameter P DO Next, set the DO's signature key pair (pk B ,sk B ), finally, generate the data summary H(Y)=(H(y1),H(y2),...,H(y n )).

[0053] Step 1.4, C1, C2 initialization: C1 and C2 generate corresponding signature key pairs and Used to digitally sign data.

[0054] Step 2: DU submits credentials α and β to the system consensus node for registration. After successful registration, it obtains the required parameters from the chain, generates a signature verification key pair and publishes the public key.

[0055] Specifically, the specific process of step 2 is as follows: DU randomly selects authorization parameters α, β as its identity credentials to register with the system, and obtains the required parameters from the chain after successful registration; generates the corresponding signature key pair (pk A ,sk A ) and publish the public key pk A . Note: Parameters α and β are unique.

[0056] Step 3: DO and DU select random numbers as blinding factors respectively, and use the public parameter PP and the blinding factor to encrypt the data to generate the obfuscated ciphertext A, A′, T,. After signing the obfuscated ciphertext A, A′, T, Q, the data is packaged by class and sent to C1 and C2 respectively.

[0057] Specifically, the specific process of step 3 is:

[0058] Step 3.1, DU preprocesses: DU first preprocesses its private data and selects a random number The ciphertext is calculated as follows:

[0059]

[0060] Let A′=(A -1 ,A -2 ,...,A -m ), then calculate a=H1(α) β , for ciphertext A and A -i Generate signatures σ respectively A =Sign(sk A ,A),σ′ A =Sign(sk A ,A′), finally, DU will (A,a,σA ) is sent to C1, and (A′,a,σ′ A ) is sent to C2.

[0061] Step 3.2, DO performs data preprocessing: DO selects random numbers The ciphertext is calculated as follows:

[0062]

[0063] Next, let T = (T1, T2, ..., T n ),Q=(Q1,Q2,...,Q n ), generate signatures σ for T and Q respectively B =Sign(sk B ,T),σ′ B =Sign(sk B ,Q). Finally, DO will (T,σ B ), (Q, σ′ B ) are sent to C1 and C2 respectively.

[0064] Step 4: C1 and C2 package the data (A, a, σ) of DO and DU. A ), (A′,a,σ′ A ), (T, σ B ), (Q, σ′ B ) and the corresponding user’s public key pk as input, and output a Boolean value of 1 or 0; when all results are 1, C1 and C2 take the packed data sent by DO and DU and the permutation matrix M1 and M2 as input respectively to generate the obfuscated ciphertext U and W.

[0065] Specifically, the specific process of step 4 is:

[0066] Step 4.1, signature verification: After C1 and C2 receive data from DO and DU, they perform signature verification. C1 calculates SVerify(pk A ,A,a,σ A ),SVerify(pk B ,T,σ B ); C2 calculates SVerify(pk A ,A′,a,σ′ A ),SVerify(pk B ,Q,σ′ B ), when all results are 1, the verification is successful, otherwise the verification fails. After the verification is successful, C1 and C2 execute the delegated computing task, otherwise it terminates.

[0067] Step 4.2, execute the entrusted computing task: C1 Calculation U j=H(e(T j ,A)), and multiply the result with the permutation matrix M1 to obtain u=(U1,U2,...,U n ),for C2 calculates W ij =H(e(Q j ,A -i )), and multiply the result by the permutation matrix M2 to obtain W = (W 11 ,W 12 ,...,W mn ).

[0068] Step 4.3, generate zk-SNARKs proof: After completing the entrusted computing task, the cloud server needs to prove the computing operations and results. C1 and C2 respectively call the zero-knowledge succinct non-interactive knowledge proof generation algorithm to generate proofs π1=Prove(pp,(T,A),U,ω1) and π2=Prove(pp,(Q,A′),W,ω2).

[0069] Step 4.4, sign the data: C1 and C2 summarize all the corresponding data in parallel, package and sign them, and generate a signature

[0070] Step 5: C1 and C2 perform authorization verification on DU. After verification, the obfuscated ciphertext U and W are sent to DU. After receiving the data, DU performs signature verification and zero-knowledge proof verification.

[0071] Specifically, the specific process of step 5 is:

[0072] Step 5.1, Authorization Verification: The cloud server authenticates the DU. If the user is an authorized user, it is allowed to access the data. Otherwise, the data use application is rejected. The details are as follows: C1, C2 calculate the verification information v = F DH-OPRF (α) and token b = a γ , and then send the token b to DU; DU uses the token b issued by the cloud server to randomize the corresponding account information into a verification code v', that is, calculate Then v′ is sent to cloud servers C1 and C2 respectively; finally, C1 and C2 verify whether the verification code v′ of DU is consistent with its corresponding verification information v. If v=v′, C1 will package the data Send to DU, C2 will package the data Sent to DU, if v≠v′, the protocol terminates.

[0073] Step 5.2, data correctness and integrity verification: After receiving the packaged data from the cloud server, DU performs operation verification and signature verification. If the verification passes, it continues the calculation as follows: DU uses the public key of the cloud server calculate and To perform signature verification, if the results are all 1, the verification is successful and the calculation continues. After the signature verification is successful, DU verifies the zk-SNARKs proof of the cloud server and calculates PVerify(pp,(T,A),U,π1) and PVerify(pp,(Q,A′),W,π2) respectively. If the results are all 1, the verification is successful and the user accepts the result returned by the cloud server.

[0074] Step 6: After all the verifications in step 5 are passed, Du performs statistical information analysis based on the received data.

[0075] Specifically, the specific process of step 6 is:

[0076] DU performs intersection cardinality calculation based on the received data to obtain the required statistical information. Finally, DU outputs the following information:

[0077]

[0078] Step 7: When the authorization verification succeeds, the access record AR is output with the identity information of DU, the tracking key k, and the data digest H(Y) as input, and then DO signs it.

[0079] Specifically, the specific process of step 7 is:

[0080] After the authorization verification is successful, the identity information of DU is encrypted and an access record is generated and stored off-chain. Specifically, the identity information of DU is encrypted through the AES encryption mechanism to obtain the ciphertext ID=Enc(k,ID), where k is the key and the access record is defined as: AR=(ID,H(Y)); then, DO needs to sign the access record and obtain the signature σ=Sign(sk A ,AR); Finally, DU uploads the data summary H(Y) to the blockchain, stores (AR, σ) off-chain, and implements a rotation mechanism for logs. During rotation, each access record is guaranteed to contain the encrypted hash value of the previous log to ensure the integrity of the log chain, and the old logs are archived to cold storage to reduce the access frequency. At the same time, segmented storage is used to further improve data security.

[0081] Step 8: When a security incident such as a data breach occurs, the applicant who meets the application requirements initiates an audit request to trace the access records for auditing.

[0082] Specifically, the specific process of step 8 is as follows: Application conditions: DO or DU that passes authorization verification and correctly submits data summary; the qualified requester applies to obtain access logs, triggering the audit process: DO first performs signature verification and calculates SVerify(pk A ,AR,σ), if the result is 1, the verification is passed. After the verification is passed, the ciphertext is decrypted to obtain id=Dec(k,ID), and finally the access log plaintext is provided for review; otherwise, an additional file damage warning will be returned.

[0083] Next, we will compare and analyze the advantages of this application with related technologies from three aspects: theoretical analysis, functional comparison and experimental evaluation. The related technologies include: [1] Y. Yang, Y. Yang, X. Chen, X. Dong, Z. Cao, and J. Shen, "Dmpsi: Efficient scalable delegated multiparty psi and psi-ca with oblivious prf," IEEE Transactions on Services Computing, 2024; [2] A. Wu, X. Xin, J. Zhu, W. Liu, C. Song, and G. Li, "Cloud-assisted laconic private set intersection cardinality," IEEE Transactions on Cloud Computing, 2024; [3] AA Jolfaei, H. Mala, and M. Zarezadeh, "Eo-psi-ca: Efficient out sourced private set intersection cardinality," Journal of Information Security and Applications, vol.65, p.102996, 2022; [4] S.Lv, J.Ye, S.Yin, X.Cheng, C.Feng, X.Liu, R.Li, Z.Li, Z.Liu, andL.Zhou, "Unbalanced private set intersection cardinality protocol with low communication cost," Future Generation Computer Systems, vol.102, pp.1054–1061, 2020; [5] S.Sharma, Y.Li, S.Mehrotra, N.Panwar, P.Gupta, and D.Ghosh, “Prism: Privacy-preserving and verifiable set computation over multiowner secret sharedoutsourced databases,” IEEE Transactions on Dependable and Secure Computing, vol.21, no.3, pp.1355–1371, 2023;[6]X.Liu,R.Wang,L.Peng,D.Luo,G.Xu,X.-B.Chen,N.Xiong,and .

[0084] First, theoretical analysis: This application is compared with related technologies. These schemes use different technologies, including OPRF, Bloom filter, homomorphic encryption, NTRU encryption, secret sharing, etc. The computational complexity and communication complexity are shown in Table 1, while Table 2 compares the computational cost of this application with other schemes.

[0085] Table 1 Complexity analysis

[0086]

[0087] Scheme [3] proposed two protocols with OPRF as the main component. This application only compares with the cardinality calculation related protocol (DMPSI-CA). The computational complexity of the protocol is mainly determined by the hashing, PRF calculation and set intersection operations in each stage. The final complexity is about O(m·d). The communication complexity is proportional to the size of the data set m or n. The overall complexity is O(m·d·ω), where d is the size of the hash bucket and ω is the size of the PRF output. In scheme [2], the sender needs to perform exponential operations and bilinear mapping operations during the execution of the protocol. The bilinear pairing complexity is O(n 2 ), n is the number of data elements involved in the operation. The receiver also needs to perform similar exponential operations and hash operations. Since m>n, the overall computational complexity is recorded as O(m 2 ). In terms of communication cost, the sender has to transmit a series of encrypted messages to the cloud server in each session, and the communication complexity is proportional to the data size n. Similarly, the receiver receives and processes messages from the cloud server and other senders. Therefore, the communication complexity is related to the data set size m and the total number of messages received, and the communication complexity can be expressed as O(m). Overall, the computational operations of the protocol are relatively fast and the overall complexity is moderate. The communication complexity is linearly related to the data set size and the number of participants. In addition, in scheme [5], the addition and multiplication properties of secret sharing are used to preload data onto the server, and its computational complexity is higher than that of the scheme of this application.

[0088] Table 2 Total computation time cost of different protocols

[0089]

[0090] Note: T Exp ,T Hash and T Mul Represents the time of an exponential, hash and multiplication operation respectively.

[0091] When analyzing the communication complexity of the proposed protocol, the focus is on the amount of information exchanged between the participants. Assuming that the protocol involves the transmission of multiple data sets, the amount of data sent by each party is proportional to the size of the data set, so the communication complexity is O(n+m), where n and m are the sizes of the data sets of the participants, respectively. At the same time, the data owner only needs to perform a single message transmission without subsequent participation, which significantly reduces the overall communication overhead during multiple protocol executions. In terms of computational cost, since the costly bilinear pairing calculations are delegated to the cloud server, the computational complexity of ILS is mainly concentrated on data processing such as generating data summaries and encryption operations of the participants. Similarly, the data owner only needs to perform the calculation once, and then it can be reused many times during the execution of the protocol, which greatly reduces the computational amortization cost during multiple protocol executions. Overall, the efficiency of this application is relatively high and can meet the actual needs of the logistics industry.

[0092] Second, functional comparison: see Table 3 for functional comparison. Solution [6] cannot calculate and store data in the cloud, which is not conducive to large-scale data calculation in practical applications. Solutions [1], [2], and [4] introduce cloud servers to reduce the burden of local computing, but these protocols are all established under the assumption that the cloud server is honest. From a practical point of view, this is weaker than other protocols. Solution [5] takes this into account and implements verification of cloud server calculations, but the protocol does not consider verification of transmission results and requires secure channels to transmit data to prevent potential attacks by eavesdroppers.

[0093] Table 3 Functional comparison of different solutions

[0094]

[0095] Among them: √ means support, × means the opposite. n1 :Data outsourcing; F n2 : Verifiable; F n3 : Non-secure channel transmission; F n4 : Access control; F n5 : Audit trail.

[0096] This application randomizes private data before uploading it to the server, and does not require any trusted entity to calculate the plaintext data, so that the data stored in the cloud appears as indistinguishable random numbers, thereby achieving privacy security. Secondly, this application verifies the integrity and correctness of the transmission results through signatures. Even if a network attacker intercepts the transmitted data and destroys it, the real information cannot be obtained, and the receiver can detect the attacker's behavior. In addition, the protocol also supports server-side authorization verification of user identity, which enhances the protection of server computing resources and ensures the security of private information. Finally, this application supports visitor tracking to ensure traceability and accountability for data use. Overall, these features together build a solid security architecture that effectively reduces the risks of data interception, unauthorized access, untrusted servers, or calculation errors. It ensures the integrity and confidentiality of shared logistics information and is suitable for practical applications in logistics-related companies.

[0097] Third, experimental evaluation: This application is compared with related technologies through experiments. The experimental environment is configured on a machine equipped with an AMD Ryzen 5 3500U processor, 16-GB RAM and Linux operating system. The protocol is written in Python language, and bilinear pairing is instantiated by the bls12_381 elliptic curve based on the py_ecc library, providing about 128 bits of security. The selected hash function is SHA-256. Blockchain development is carried out using Hyperledger Composer, and chain code is written in Go language. Hyperledger Fabric is managed by containerization through Docker and Docker Compose. In terms of network configuration, the default port settings are used to facilitate communication between containers. In ILS, cloud servers have a large amount of computing resources and communication bandwidth, so when performing performance evaluation, only the computing burden and communication cost of data owners and users are considered. In addition, for scheme [1], let k = 7, sp = 112, so that the false positive probability of the Bloom filter is minimized and the overall performance is optimal.

[0098] As shown in FIG3, the present application is compared with solutions [1], [2], and [3] in terms of 10 To 2 20The computational overhead under different set sizes. As can be seen from Figure 3(a), the computational complexity of the data owner in each protocol is polynomial. However, in actual applications, when an enterprise faces multiple protocol execution applications, as a data owner, it does not need to recalculate the required data and can quickly update the message. Then as the number of protocol executions increases, the amortized computational cost of a protocol will decrease. As can be seen from Figure 3(b), the computational cost of the data user is linearly related to the size of the input data. In general, as shown in Figure 3(c), schemes [2] and [3] have higher computational costs than our protocol. As the amount of data increases, the total computational cost of the participants in scheme [1] is lower than that of this application. However, in actual applications, facing large sets of data, this application still has a better computational burden performance and has more practical features than scheme [1] which only performs intersection calculation operations.

[0099] Figure 4 shows the comparison of the communication costs of various protocols. As can be seen from Figure 4, as the amount of input data increases, the communication overhead of each party increases linearly. Among them, the communication burden of scheme [1] is the largest. When the input data set size is 2 10 In the case of [2], it takes about 10MB. In this application, because the data user needs to interact with the cloud server for authorization and authentication, the communication cost is slightly higher than that of the data owner. As shown in Figure 4(c), overall, the communication cost of this application is similar to that of solution [2]. However, in practical applications, this application can resist malicious attacks and has higher security.

[0100] Finally, Figure 5 shows that the user dataset size is fixed to 2. 10 The performance test of the blockchain at this time. Figure 5(a) shows the trend of transaction throughput changes under different loads. This application simulates multiple transaction requests with different numbers of concurrent users and records the transaction success rate and response time in each case. It can be seen that the blockchain transaction throughput of ILS shows a trend of first increasing and then stabilizing with the increase of concurrent users. When the number of concurrent users reaches about 15, it reaches saturation, and the transaction processing speed begins to decrease, resulting in a slight decrease in throughput. The resource consumption is shown in Figure 5(b) and Figure 5(c). Docker's resource monitoring is used to record the resource consumption under different loads. The indicators shown in Figure 5 show that as the number of concurrent users increases, the CPU, memory, and storage usage all increase within a reasonable range. This shows that ILS can effectively utilize CPU resources to handle increased concurrent requests, and the blockchain design is relatively efficient.

[0101] In summary, the present application has low computing and communication overhead and high security, and is suitable for logistics companies with scarce computing resources and huge amounts of data.

[0102] Corresponding to the above embodiment, the present invention also proposes an electronic device, which includes a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, the above-mentioned blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method is implemented.

[0103] The electronic device of the embodiment of the present invention improves the security of user data by implementing the intelligent logistics authorization cloud-assisted PSI-CA method supporting blockchain.

[0104] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0105] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0106] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0107] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A blockchain-supported intelligent logistics authorization cloud-assisted PSI-CA method, characterized in that: It includes multiple entities: data owner DO, data user DU, blockchain BC, first cloud server C1 and second cloud server C2; The method comprises: Step 1: The consensus node of BC takes the security parameter λ as input and outputs the public parameter PP; the accounting node of BC takes the public parameter PP and the parameter r uploaded by DO as input and outputs the parameter P required by each participant. DO ,P DU ,pp,γ,DO,C1 and C2 set their respective signature keys (pk B ,sk B ), and and publish the public key; Step 2: DU submits credentials α and β to the system consensus node for registration. After successful registration, it obtains the required parameters from the chain, generates a signature verification key pair and publishes the public key. Step 3, DO and DU respectively select random numbers as blinding factors, and use the public parameter PP and the blinding factor to encrypt data to generate obfuscated ciphertext A, A′, T, Q. After signing the obfuscated ciphertext A, A′, T, Q, the data is packaged by class and sent to C1 and C2 respectively; Step 4: C1 and C2 package the data (A, a, σ) of DO and DU. A ), (A′,a,σ′ A ), (T, σ B ), (Q, σ′ B ) and the corresponding user's public key pk as input, and output a Boolean value of 1 or 0; when all results are 1, C1 and C2 take the packaged data sent by DO and DU and the permutation matrix M1 and M2 as input respectively to generate the obfuscated ciphertext U and W; Step 5: C1 and C2 perform authorization verification on DU. After verification, they send the obfuscated ciphertext U and W to DU. After receiving the data, DU performs signature verification and zero-knowledge proof verification. Step 6: After all the verifications in step 5 are passed, the DU performs statistical information analysis based on the received data; Step 7: When the authorization verification succeeds, the DU's identity information, tracking key k, and data digest H(Y) are used as input to output the access record AR, which is then signed by DO. Step 8: When a security incident such as a data breach occurs, the applicant who meets the application requirements initiates an audit request to trace the access records for auditing.

2. The blockchain-enabled intelligent logistics authorization cloud-assisted PSI-CA method according to claim 1 is characterized in that: The specific process of step 1 is as follows: Step 1.1, overall initialization: BC consensus nodes generate public parameters PP = {e, p, g1, g2, G1, G2, G T ,G,H,H1,H2,λ}, define a bilinear mapping e:G1×G2→G T , where G1, G2 and G T is a group of order p, g1 and g2 are generators of groups G1 and G2, and three collision-resistant hash functions are defined: H: {0,1} * →{0,1} λ , H1: {0,1} * →G, H2: G→{0,1} λ , where G is the elliptic curve group, λ is the security parameter, which represents the fixed length of the binary string and is used to output the final pseudo-random value; Step 1.2, Node Initialization: Different accounting nodes on BC are responsible for different parameter generation and distribution. First, generate the parameters of DU Next, generate the parameters of DO And the parameters required for zero-knowledge proof pp = Setup (1 λ ,C), track the key k, and finally generate the key γ of the oblivious pseudo-random function for identity authentication; Step 1.3, DO initialization: choose a random number Upload it to BC to generate parameter P DO Next, set the DO's signature key pair (pk B ,sk B ), finally, generate the data summary H(Y)=(H(y1),H(y2),...,H(y n )); Step 1.4, C1, C2 initialization: C1 and C2 generate corresponding signature key pairs and Used to digitally sign data.

3. The blockchain-enabled intelligent logistics authorization cloud-assisted PSI-CA method according to claim 2 is characterized in that: The specific process of step 2 is as follows: DU randomly selects authorization parameters α, β as its identity credentials to register with the system. After successful registration, it obtains the required parameters from the chain; generates the corresponding signature key pair (pk A ,sk A ) and publish the public key pk A .

4. The blockchain-enabled intelligent logistics authorization cloud-assisted PSI-CA method according to claim 3 is characterized in that: The specific process of step 3 is as follows: Step 3.1, DU preprocesses: DU first preprocesses its private data and selects a random number The ciphertext is calculated as follows: Let A′=(A -1 ,A -2 ,...,A -m ), then calculate a=H1(α) β , for ciphertext A and A -i Generate signatures σ respectively A =Sign(sk A ,A),σ′ A =Sign(sk A ,A′), finally, DU will (A,a,σ A ) is sent to C1, and (A′,a,σ′ A ) is sent to C2; Step 3.2, DO performs data preprocessing: DO selects random numbers The ciphertext is calculated as follows: Next, let T = (T1, T2, ..., T n ),Q=(Q1,Q2,...,Q n ), generate signatures σ for T and Q respectively B =Sign(sk B ,T),σB′ B =Sign(sk B ,Q), and finally, DO will (T,σ B ), (Q, σ′ B ) are sent to C1 and C2 respectively.

5. The blockchain-enabled intelligent logistics authorization cloud-assisted PSI-CA method according to claim 4 is characterized in that: The specific process of step 4 is as follows: Step 4.1, signature verification: After C1 and C2 receive data from DO and DU, they perform signature verification. C1 calculates SVerify(pk A ,A,a,σ A ),SVerify(pk B ,T,σ B ); C2 calculates SVerify(pk A ,A′,a,σ′ A ),SVerify(pk B ,Q,σ′ B ), when all results are 1, the verification is successful, otherwise the verification fails. After the verification is successful, C1 and C2 execute the entrusted computing task, otherwise it terminates; Step 4.2, execute the entrusted computing task: Calculate U j =H(e(T j ,A)), and multiply the result with the permutation matrix M1 to obtain U=(U1,U2,...,U n ),for j∈[n], C2 calculates W ij =H(e(Q j ,A -i )), and multiply the result by the permutation matrix M2 to obtain W = (W 11 ,W 12 ,...,W mn ); Step 4.3, generate zk-SNARKs proof: After completing the entrusted computing task, the cloud server needs to prove the computing operation and the computing result. C1 and C2 respectively call the zero-knowledge succinct non-interactive knowledge proof generation algorithm to generate proofs π1=Prove(pp,(T,A),U,ω1) and π2=Prove(pp,(Q,A′),W,ω2). Step 4.4, sign the data: C1 and C2 summarize all the corresponding data in parallel, package and sign them, and generate a signature 6. The blockchain-enabled intelligent logistics authorization cloud-assisted PSI-CA method according to claim 5 is characterized in that: The specific process of step 5 is as follows: Step 5.1, Authorization Verification: The cloud server authenticates the DU. If the user is an authorized user, it is allowed to access the data. Otherwise, the data use application is rejected. The details are as follows: C1, C2 calculate the verification information v = F DH-OPRF (α) and token b = a γ , and then send the token b to DU; DU uses the token b issued by the cloud server to randomize the corresponding account information into a verification code v', that is, calculate Then v′ is sent to cloud servers C1 and C2 respectively; finally, C1 and C2 verify whether the verification code v′ of DU is consistent with its corresponding verification information v. If v=v′, C1 will package the data Send to DU, C2 will package the data Sent to DU, if v≠v′, the protocol terminates; Step 5.2, data correctness and integrity verification: After receiving the packaged data from the cloud server, DU performs operation verification and signature verification. If the verification passes, it continues the calculation as follows: DU uses the public key of the cloud server calculate and To perform signature verification, if the results are all 1, the verification is successful and the calculation continues. After the signature verification is successful, DU verifies the zk-SNARKs proof of the cloud server and calculates PVerify(pp,(T,A),U,π1) and PVerify(pp,(Q,A′),W,π2) respectively. If the results are all 1, the verification is successful and the user accepts the result returned by the cloud server.

7. The blockchain-enabled intelligent logistics authorization cloud-assisted PSI-CA method according to claim 6 is characterized in that: The specific process of step 6 is as follows: DU performs intersection cardinality calculation based on the received data to obtain the required statistical information. Finally, DU outputs the following information:

8. The blockchain-enabled intelligent logistics authorization cloud-assisted PSI-CA method according to claim 7 is characterized in that: The specific process of step 7 is as follows: After the authorization verification is successful, the identity information of DU is encrypted and an access record is generated and stored off-chain. Specifically, the identity information of DU is encrypted through the AES encryption mechanism to obtain the ciphertext ID=Enc(k,ID), where k is the key and the access record is defined as: AR=(ID,H(Y)); then, DO needs to sign the access record and obtain the signature σ=Sign(sk A ,AR); finally, DU uploads the data summary H(Y) to the blockchain, stores (AR, σ) off-chain, and implements a rotation mechanism for the logs. During the rotation, each access record is guaranteed to contain the encrypted hash value of the previous log to ensure the integrity of the log chain. The old logs are archived to cold storage to reduce the access frequency.

9. The blockchain-enabled intelligent logistics authorization cloud-assisted PSI-CA method according to claim 7, characterized in that: The specific process of step 8 is as follows: Application conditions: DO or DU that has passed authorization verification and correctly submitted data summary; The qualified requester applies to obtain the access log, triggering the audit process: DO first performs signature verification and calculates SVerify(pk A ,AR,σ), if the result is 1, the verification is passed. After the verification is passed, the ciphertext is decrypted to obtain id=Dec(k,ID), and finally the access log plaintext is provided for review; otherwise, an additional file damage warning will be returned.

10. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: When the computer program is executed by the processor, the blockchain-enabled smart logistics authorization cloud-assisted PSI-CA method according to any one of claims 1 to 9 is implemented.

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