Self-adaptive privacy data protection method oriented to network collaborative manufacturing platform

Abstract

The invention discloses a network collaborative manufacturing platform-oriented self-adaptive privacy data protection method, which comprises the following steps executed in sequence: S1, an enterprise user encrypts privacy data by adopting a Pedersen commitment, and the network collaborative manufacturing platform acquires the Pedersen commitment of the enterprise user; s2, the network collaborative manufacturing platform performs credit scoring on each enterprise; s3, the platform obtains the request information of the enterprise user Q for obtaining the private data of the enterprise user P, the network collaborative manufacturing platform sends the request information of the enterprise user Q and the credit score of the enterprise user Q to the enterprise user P, and the platform receives the private data description sent by the enterprise user P and sends the private data description to the enterprise user Q; if the enterprise user Q initiates a verification request, executing the step S4; s4, the network collaborative manufacturing platform verifies the privacy data description sent by the enterprise user P, and sends a verification result to the enterprise user Q; and the privacy data of the enterprise can be well protected in most cases.

Description

Technical Field

[0001] The present invention belongs to the field of privacy data protection, and in particular relates to an adaptive privacy data protection method for a network collaborative manufacturing platform. Background Art

[0002] Network collaborative manufacturing is an important path to promote the transformation and upgrading of the manufacturing industry. It realizes the comprehensive interconnection and efficient collaboration of manufacturing resources, capabilities, and processes based on the Internet, reduces manufacturing costs by improving quality and efficiency, and thus enhances the competitive advantages of industrial agglomeration areas.

[0003] Generally speaking, network collaborative manufacturing needs to be led by government departments to build and organize a web platform to call on various enterprises upstream and downstream of the supply chain to settle in and complete the digitization of production factors such as resources on the platform. Then, on this basis, core network collaborative manufacturing services such as business matchmaking, precise dispatching, centralized procurement and supply chain finance are provided, while providing a certain degree of government endorsement and supervision during the service process. However, due to the complex trust issues involved in commercial transactions, some companies regard key data required for collaborative manufacturing such as daily production capacity and inventory as private data and are unwilling to disclose them on the platform, or even do not want the platform to know them to prevent the exposure of commercial secrets. In this case, how such companies can participate in network collaborative manufacturing and meet the regulatory needs of the platform is a key issue to be resolved. Summary of the invention

[0004] The purpose of the present invention is to provide an adaptive privacy data protection method for a network collaborative manufacturing platform that can protect enterprise privacy.

[0005] In order to achieve the above object, the present invention adopts such technical solution: An adaptive privacy data protection method for a network collaborative manufacturing platform includes the following steps performed in sequence: S1: Enterprise users use Pedersen commitment to encrypt private data, and the network collaborative manufacturing platform obtains the Pedersen commitment of enterprise users; S2: Construct the credit scoring function shown in the following formula (1) to assess the credit score of each enterprise user in the network collaborative manufacturing platform : (1); in, and They represent the number of transactions that the enterprise users have made as buyers and sellers since they joined the platform. Indicates The amount of the transaction, Indicates that enterprise users The number of satisfied reviews received in transactions, Indicates The credibility of the reviews in the transaction, and They represent the weights of the buyer's credit and the seller's credit of the enterprise user in a credit assessment. ; S3: The network collaborative manufacturing platform obtains the request information of the enterprise user Q for obtaining the private data of the enterprise user P. The network collaborative manufacturing platform sends the request information of the enterprise user Q and the credit score of the enterprise user Q to the enterprise user P. The network collaborative manufacturing platform receives the private data description sent by the enterprise user P and sends the private data description to the enterprise user Q. If the enterprise user Q initiates a verification request, step S4 is executed. S4: The network collaborative manufacturing platform verifies the privacy data description sent by enterprise user P and sends the verification result to enterprise user Q.

[0006] Preferably, in step S1, the enterprise user uses a Pedersen commitment based on an elliptic curve to encrypt the private data, and the enterprise user selects a random natural number , i.e., the blinding factor, generates the following Pedersen commitment for the privacy data: : (2); Among them, G and H are two random points of the elliptic curve, and X is the private data.

[0007] Preferably, the credit score is calculated using the following formula (3): Normalize to the range [0,1]: (3); Among them, e is a natural constant.

[0008] Preferably, in step S3, the privacy data is described as one of the following three descriptions: , , , in, , B= .

[0009] Preferably, the privacy data description verification process in step S4 is as follows: When the privacy data provided by enterprise user P is described as When 、A、 and , the network collaborative manufacturing platform verifies the following formula (4): + == (4); in, , ; Simultaneous verification and send the verification result to the enterprise user Q; When the privacy data provided by enterprise user P is described as When , B, and , the network collaborative manufacturing platform verifies the following formula (5): + == (5); , , For Blinding factor for Pedersen commitment; Simultaneous verification and send the verification result to the enterprise user Q; When the privacy data provided by enterprise user P is described as When 、A、 , , , B, and ,The network collaborative manufacturing platform verifies formula (4) and formula (5), and sends the verification results to the enterprise user Q.

[0010] An adaptive privacy data protection system includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, any of the above methods is implemented.

[0011] A computer storage medium stores a computer program, which implements any of the above methods when executed by a processor.

[0012] A computer product comprises a computer program, which implements any of the above methods when executed by a processor.

[0013] By adopting the above-mentioned design scheme, the beneficial effect of the present invention is as follows: the present application designs a set of privacy data protection methods and processes using multiple encryption technologies and characteristics in response to the privacy data protection needs of enterprises on the network collaborative manufacturing platform and the regulatory needs of the platform; first, the enterprise user uses the Pedersen commitment to encrypt the specific data that needs to be protected; then, when other enterprise users need to view the privacy data, the network collaborative manufacturing platform first performs a credit score on it and sends the credit score to the enterprise user. The enterprise decides by itself what description method to use to send the privacy data to the enterprise user who requests to view it. When and only when a dispute arises, the platform can require the enterprise user to open the corresponding Pedersen commitment and clearly verify the corresponding privacy data. This method and process can protect the privacy data of the enterprise well in most cases, and can support the platform to audit the privacy data in a few special cases. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 For the present invention Data interaction process in the case of Figure 2 For the present invention The data interaction process in this case. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only partial embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] An adaptive privacy data protection method for a network collaborative manufacturing platform includes the following steps performed in sequence: S1: Enterprise users use Pedersen commitment to encrypt private data, and the network collaborative manufacturing platform obtains the Pedersen commitment of enterprise users; In this embodiment, in step S1, the enterprise user uses the Pedersen commitment based on the elliptic curve to encrypt the private data. The enterprise user selects a random natural number , i.e. the blinding factor, generates the following Pedersen commitment for the privacy data: : (2); Among them, G and H are two random points of the elliptic curve, and X is the private data.

[0017] S2: Construct the credit scoring function shown in the following formula (1) to assess the credit score of each enterprise user in the network collaborative manufacturing platform : (1); in, and They represent the number of transactions that the enterprise users have made as buyers and sellers since they joined the platform. Indicates The amount of the transaction, Indicates that enterprise users The number of satisfied reviews received in transactions, Indicates The credibility of the reviews in the transaction, and They represent the weights of the buyer's credit and the seller's credit of the enterprise user in a credit assessment. In this embodiment, , , , In the network collaborative manufacturing platform, it is possible that the enterprise users who have settled in the platform have always been buyers or sellers. and It is set in advance by the user company based on its own preferences.

[0018] In this embodiment, the following formula (3) is used to calculate the credit score: Normalize to the range [0,1]: (3); Among them, e is a natural constant.

[0019] According to formula (2) and formula (3), when enterprise users enter the platform and Both are 0, so the initial credit is calculated to be 0.5.

[0020] S3: The network collaborative manufacturing platform obtains the request information of enterprise user Q for obtaining the private data of enterprise user P. The network collaborative manufacturing platform sends the request information of enterprise user Q and the credit score of enterprise user Q to enterprise user P. The network collaborative manufacturing platform receives the private data description sent by enterprise user P and sends the private data description to enterprise user Q. If enterprise user Q initiates a verification request, step S4 is executed. In this embodiment, in step S3, the private data description is one of the following three descriptions: , , , in, , B= .

[0021] The privacy data protection method of this application is only applicable to the protection of digital data.

[0022] S4: The network collaborative manufacturing platform verifies the privacy data description sent by enterprise user P and sends the verification result to enterprise user Q.

[0023] In this embodiment, the privacy data description verification process in step S4 is as follows: like Figure 1 As shown, when the privacy data provided by enterprise user P is described as When 、A、 and , the network collaborative manufacturing platform verifies the following formula (4): + == (4); in, , , For Blinding factor for Pedersen commitment.

[0024] If the verification of formula (4) holds, the network collaborative manufacturing platform does not know Under the premise of , otherwise the verification fails.

[0025] Next, we just need to verify , you can verify , and sends the verification result to the enterprise user Q; almost all zero-knowledge range proof methods (Zero Knowledge Range Proof), such as Borromean ring signature, ZK-SNARK and Bulletproofs, can be used to verify that a secret integer is not less than 0.

[0026] like Figure 2 As shown, when the privacy data provided by enterprise user P is described as When , B, and , the network collaborative manufacturing platform verifies the following formula (5): + == (5); in, , , For Blinding factor for Pedersen commitment; If the verification of formula (5) holds, the network collaborative manufacturing platform does not know Under the premise of , otherwise the verification fails.

[0027] Similarly, we only need to use the zero-knowledge range proof method to verify , you can verify and send the verification result to the enterprise user Q; When the privacy data provided by enterprise user P is described as When 、A、 , , , B, and ,The network collaborative manufacturing platform verifies formula (4) and formula (5), and sends the verification results to the enterprise user Q.

[0028] It should be pointed out that although there are zero-knowledge range proof methods that can be used to verify that an integer belongs to a certain interval or , but restrictions must be a power of 2, or and It can be any integer, but it requires more computation and communication overhead. Because almost all zero-knowledge range proof methods can verify that a secret integer is not less than 0, the method and process of this application can, firstly, the network collaborative manufacturing platform can decide which zero-knowledge range proof method to use in a unified framework based on the developer's familiarity with encryption technology and the network collaborative manufacturing platform's tolerance for computation and communication overhead, etc., which has very high flexibility and scalability; secondly, the computation and communication overhead of existing zero-knowledge range proof methods are positively correlated with the binary length of the secret number, so by verifying the zero-knowledge range proof method, the network collaborative manufacturing platform can determine which zero-knowledge range proof method to use in a unified framework based on the developer's familiarity with encryption technology and the network collaborative manufacturing platform's tolerance for computation and communication overhead, etc. or Proving a range is usually more efficient than directly There is less overhead in performing range proofs, as the former is often smaller than .

[0029] The present application also provides a system for a user to implement the above method.

[0030] An adaptive privacy data protection system includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, any of the above methods is implemented.

[0031] The present application also provides a storage medium storing a computer program capable of implementing the above method.

[0032] A computer storage medium stores a computer program, which implements any of the above methods when executed by a processor.

[0033] The present application also provides a computer program product for implementing the above method.

[0034] A computer product comprises a computer program, which implements any of the above methods when executed by a processor.

[0035] In this embodiment, the above method is further illustrated by the following example.

[0036] For example, enterprise user P wants to protect inventory data and submit the information "inventory quantity is greater than 10,000 pieces" to the network collaborative manufacturing platform. The specific steps include: (1) In the commitment phase, the network collaborative manufacturing platform sets the public parameters of the Pedersen commitment. Specifically, an elliptic curve with proven security (such as Sm2p256v1, secp256k1, etc.) can be selected, and then two different random points are randomly selected as and .

[0037] Enterprise user P generates a commitment: the privacy-protected inventory is , then user enterprise P only needs to submit The calculation results are sent to the platform.

[0038] (2) In the information disclosure stage, if enterprise user Q wants to view the inventory data of enterprise user P, it first sends a request to the network collaborative manufacturing platform; the network collaborative manufacturing platform calculates the credit score of enterprise user Q based on historical data. , and credit score R Sent to enterprise user P; enterprise user P selects the privacy data description and sends it to the network collaborative manufacturing platform, and the network collaborative manufacturing platform receives the privacy data description sent by enterprise user P. " and sends the privacy data description to enterprise user Q; enterprise user Q questions the received privacy data description and initiates a verification request: The network collaborative manufacturing platform is based on the enterprise user P , The calculation results and , independent calculation , then verify + Is it equal to If the verification is successful, it means , the platform still needs to verify Is it greater than 0? Taking the most efficient Bulletproofs method and its open source implementation "Tongsuo" (https: / / github.com / Tongsuo-Project / Tongsuo) as an example, and assuming that the confidential 3000 , introducing the process of range proof: The network collaborative manufacturing platform uses the following instructions to generate a public parameter file pp.pem and sends it to the user enterprise P or makes it publicly available for all users to download.

[0039]

[0040] The user enterprise generates the evidence file witness_range.pem using the following command based on the public parameter file pp.pem.

[0041]

[0042] Based on the evidence file, the user enterprise further generates a range evidence file proof-range.pem, which is , and sent to the network collaborative manufacturing platform.

[0043]

[0044] The network collaborative manufacturing platform verifies the range proof proof-range.pem sent by the enterprise user based on the public parameter file pp.pem. If the following verification command returns "The proof is valid", the verification is successful, otherwise the verification fails.

[0045] Network Collaborative Manufacturing Platform Verification If the verification is successful, it means If it is not less than 0, the verification is successful and the verification result is sent to the enterprise user Q. Otherwise, the verification failure result is sent to the enterprise user Q.

[0046] In summary, this application designs a set of privacy data protection methods and processes that utilize multiple encryption technologies and features to address the privacy data protection needs of enterprises on the network collaborative manufacturing platform and the regulatory needs of the platform; first, enterprise users use Pedersen commitments to encrypt specific data that need to be protected; then, when other enterprise users need to view the privacy data, the network collaborative manufacturing platform first performs a credit score on it and sends the credit score to the enterprise user. The enterprise decides by itself what description method to use to send the privacy data to the enterprise user who requests to view it. When and only when a dispute arises, the platform may require the enterprise user to open the corresponding Pedersen commitment and clearly verify the corresponding privacy data. This method and process can, in most cases, protect the privacy data of the enterprise well, and in a few special cases, support the platform to audit the privacy data.

[0047] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An adaptive privacy data protection method for a network collaborative manufacturing platform, characterized by: The steps include the following steps: S1: Enterprise users use Pedersen commitment to encrypt private data, and the network collaborative manufacturing platform obtains the Pedersen commitment of enterprise users; S2: Construct the credit scoring function shown in the following formula (1) to assess the credit score of each enterprise user in the network collaborative manufacturing platform : （1）； in, and They represent the number of transactions that the enterprise users have made as buyers and sellers since they joined the platform. Indicates The amount of the transaction, Indicates that enterprise users The number of satisfied reviews received in transactions, Indicates The credibility of the reviews in the transaction, and They represent the weights of the buyer's credit and the seller's credit of the enterprise user in a credit assessment. ; S3: The network collaborative manufacturing platform obtains the request information of the enterprise user Q for obtaining the private data of the enterprise user P. The network collaborative manufacturing platform sends the request information of the enterprise user Q and the credit score of the enterprise user Q to the enterprise user P. The network collaborative manufacturing platform receives the private data description sent by the enterprise user P and sends the private data description to the enterprise user Q. If the enterprise user Q initiates a verification request, step S4 is executed. S4: The network collaborative manufacturing platform verifies the privacy data description sent by enterprise user P and sends the verification result to enterprise user Q.

2. The adaptive privacy data protection method for a network collaborative manufacturing platform according to claim 1, characterized in that: In step S1, the enterprise user uses the Pedersen commitment based on the elliptic curve to encrypt the private data. The enterprise user selects a random natural number , i.e., the blinding factor, generates the following Pedersen commitment for the privacy data: : （2）； Among them, G and H are two random points of the elliptic curve, and X is the private data.

3. The adaptive privacy data protection method for a network collaborative manufacturing platform as claimed in claim 2, characterized in that: The credit score is calculated using the following formula (3): Normalize to the range [0,1]: （3）； Among them, e is a natural constant.

4. The adaptive privacy data protection method for a network collaborative manufacturing platform as claimed in claim 3, characterized in that: In step S3, the privacy data is described as one of the following three descriptions: ， ， ， in, , B= .

5. The adaptive privacy data protection method for a network collaborative manufacturing platform as claimed in claim 4, characterized in that: The privacy data description verification process in step S4 is as follows: When the privacy data provided by enterprise user P is described as When 、A、 and , the network collaborative manufacturing platform verifies the following formula (4): + == （4）； in, , ; Simultaneous verification and send the verification result to the enterprise user Q; When the privacy data provided by enterprise user P is described as When , B, and , the network collaborative manufacturing platform verifies the following formula (5): + == （5）； , , For Blinding factor for Pedersen commitment; Simultaneous verification and send the verification result to the enterprise user Q; When the privacy data provided by enterprise user P is described as When 、A、 , , , B, and ,The network collaborative manufacturing platform verifies formula (4) and formula (5), and sends the verification results to the enterprise user Q.

6. An adaptive privacy data protection system, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that: When the processor executes the computer program, the method described in any one of claims 1 to 5 is implemented.

7. A computer storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method described in any one of claims 1 to 5 is implemented.

8. A computer product, characterized in that: The invention comprises a computer program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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