Multi-party privacy data intersection method for commodity supply platform

Abstract

The invention relates to the technical field of privacy data interaction, in particular to a multi-party privacy data intersection method for a commodity supply platform, and the method comprises the steps: determining the data uniqueness of each character according to the occurrence frequency of each character in key selection data, and calculating the data contact unique quantity of the key selection data; calculating a hash value as a blind factor for generating a cryptographic commitment for plaintext information after performing data series construction on the data contact unique quantity by using a current timestamp; and the receiver judges whether the received plaintext information and the blind factor are tampered or not by using the received plaintext information and the blind factor sent by the sender. According to the method, the blind factor is calculated through the plaintext information, verification complexity caused by random generation of the blind factor in a traditional method is avoided, a receiver can directly find whether the data is tampered or not, multi-party confirmation is not needed, the data verification efficiency is remarkably improved, and time and calculation resources of a user are saved.

Description

Technical Field

[0001] The present application relates to the technical field of private data interaction, and in particular to a multi-party private data interaction method for a commodity supply platform. Background Art

[0002] In the multi-party privacy data interaction of commodity supply platforms, the application of privacy protection technology is crucial. With the rapid development of the data factor market, privacy computing technologies such as zero-knowledge proof, homomorphic encryption and secure multi-party computing have become key means to achieve data privacy protection. Zero-knowledge proof allows one party to prove the authenticity of a statement without leaking any information, and is widely used in financial services and blockchain privacy transactions. Homomorphic encryption supports direct calculation of encrypted data, and the encrypted result can be obtained without decryption, which is suitable for scenarios such as cloud computing and data mining. In addition, secure multi-party computing allows multiple participants to complete computing tasks together without leaking their own data, which is suitable for electronic elections, threshold signatures and electronic auctions. The combination of these technologies provides the commodity supply platform with a privacy protection effect of "interoperable and verifiable but invisible", meeting the needs of secure sharing of commercial sensitive data in the supply chain.

[0003] In the process of multi-party private data interaction, in order to ensure the sender's information hiding and binding. The traditional method generally uses a cryptographic commitment scheme (Pederse commitment) to encrypt and sign the private data. However, the traditional method generates blind factors through random methods. If the plaintext information and blind factors are tampered with when sending, it is not possible to directly determine whether the information has been tampered with. It is also necessary to calculate the commitment value and multiple parties need to confirm whether the information has been tampered with, which wastes a lot of user time and makes Pedersen commitment less effective in verifying tampered data. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a multi-party privacy data intersection method for a commodity supply platform to solve the existing problems.

[0005] The multi-party privacy data intersection method for a commodity supply platform of the present application adopts the following technical solution: An embodiment of the present application provides a method for intersecting multi-party private data on a commodity supply platform, the method comprising the following steps: S1, obtain the plain text information sent by the sender through the commodity supply platform, remove meaningless words, and use it as the key selection data; S2, the result of negative correlation mapping of the frequency of occurrence of each character in the key selection data is used as the data uniqueness of the character; S3, using the data uniqueness of non-numeric characters to perform threshold segmentation on non-numeric characters, filter out some non-numeric characters, and combine with all numeric characters, use their data uniqueness and the sequence of all their data uniqueness arranged from small to large to perform forward fusion, and use the fusion result as the data connection uniqueness of the key selection data; S4, using the current timestamp, constructing a data concatenation of the data-linked unique quantity as a key unique material of the blinding factor; S5, obtaining a hash value of the key unique material, and using the hash value as a blinding factor for cryptographic commitment generation of the plaintext information; S6, after the receiver receives the plaintext information and blinding factor sent by the sender, it calculates the blinding factor according to the above method based on the received plaintext information, and compares it with the received blinding factor to determine whether the received plaintext information and blinding factor have been tampered with.

[0006] Preferably, the method for removing meaningless words adopts a data bleaching method.

[0007] Preferably, the method for calculating the data uniqueness is further determined as follows: the data uniqueness of the i-th character is recorded as , ; In the formula, Indicates the number of all characters in the key selection data, Indicates the frequency of occurrence of the i-th character in the key selection data.

[0008] Preferably, the threshold segmentation algorithm further adopts Otsu threshold segmentation algorithm.

[0009] Preferably, the method for screening some non-numeric characters is: taking non-numeric characters whose data uniqueness is greater than a segmentation threshold as the screened out part of the non-numeric characters.

[0010] Preferably, the calculation method of the data connection uniqueness of the key selection data is further determined as: the sum of the products of all element values ​​in the data uniqueness sequence and their corresponding bit sequences.

[0011] Preferably, the hash value calculation method further adopts the SHA-256 hash algorithm.

[0012] Preferably, the cryptographic commitment method adopts Pedersen commitment.

[0013] Preferably, the method for determining whether the received plaintext information and blinding factors have been tampered with is: If the comparison result is inconsistent with the received blinding factor, the plaintext information and the blinding factor have been tampered with; at the same time, a message that the plaintext information and the blinding factor have been tampered with is sent to the sender; If the comparison result is consistent with the received blinding factor, the plaintext information and the blinding factor have not been tampered with; then the plaintext information is used to generate a cryptographic commitment using the blinding factor, and it is determined whether the commitment received by the recipient is consistent with the generated commitment.

[0014] Preferably, when the commitment received by the recipient is consistent with the generated commitment, the interacted private data is consistent; when the commitment received by the recipient is inconsistent with the generated commitment, it is considered that the information sent by the sender in the commitment phase is inconsistent with the information sent in the opening phase, and the sender's plaintext information cannot be trusted, and the sender is notified of the information inconsistency problem, and the sender is required to check it by itself.

[0015] This application has at least the following beneficial effects: 1. The present invention calculates the blinding factor through the plaintext information itself, avoiding the verification complexity caused by the random generation of blinding factors in traditional methods. When verifying the data, the receiver can directly find out whether the data has been tampered with without the need for multiple confirmations, which significantly improves the efficiency of data verification and saves the user's time and computing resources.

[0016] 2. The present invention uses the uniqueness of low-frequency characters to generate blinding factors, reducing the possibility of hash conflicts. At the same time, the unique key material of the blinding factor is generated in combination with the timestamp to ensure the uniqueness of each encryption. This mechanism not only enhances the uniqueness and unpredictability of data, but also effectively prevents attackers from inferring the original data through repeated encryption results, further improving the level of data privacy protection.

[0017] 3. The present invention realizes efficient and secure interaction of multi-party private data through an optimized commitment generation and verification mechanism. The sender generates an encrypted commitment value in the commitment phase, and the receiver verifies the data consistency in the opening phase. This "interoperable and verifiable but invisible" privacy protection effect meets the demand for secure sharing of commercial sensitive data in the supply chain and provides reliable technical guarantee for multi-party private data interaction on the commodity supply platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flowchart of a method for intersecting multi-party private data on a commodity supply platform provided by this application; Figure 2A block diagram for determining whether the received plaintext information and the blind factor are tampered and subsequent operation steps provided by this application. Detailed implementation manners

[0020] In order to further elaborate on the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following combines the drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of a method for multi-party private data intersection for a commodity supply platform proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0022] The following specifically describes the specific solution of a method for multi-party private data intersection for a commodity supply platform provided by this application with reference to the drawings.

[0023] A method for multi-party private data intersection for a commodity supply platform provided by an embodiment of this application.

[0024] In the process of multi-party private data interaction, it may be necessary for some receiving parties not to know the information in advance. In order to ensure that the information is not tampered with by the sending party after the receiving party knows the information. Therefore, it is necessary to conceal the information sent by the sending party and send the commitment value. And when it is necessary for the sending party to send the plaintext information and the blind factor, in order to prevent the plaintext information and the blind factor from being tampered with. Therefore, in this application, the blind factor is calculated through the plaintext information itself to ensure that the receiving party can directly find whether the data has been tampered with when verifying the data.

[0025] Specifically, a method for multi-party private data intersection for a commodity supply platform is provided as follows. Please refer to Figure 1 , and the method includes the following steps: S1, obtain the plaintext information sent by the sending party through the commodity supply platform, and after removing meaningless words, use it as the key selection data.

[0026] First, obtain the plaintext information sent by the sending party through the commodity supply platform. Then, remove meaningless characters in the plaintext information through the data whitening method, such as: "de", ".", ",", "le". The plaintext information after data whitening is called the key selection data. The calculation of data whitening is a well-known technology, and the specific calculation process will not be elaborated here.

[0027] S2, the result of negative correlation mapping the frequency of occurrence of each character in the key selection data is used as the data uniqueness of the character.

[0028] When generating blind factors for data sent by users of the commodity supply platform, since the data sent by the sender for the first time is the commitment value C, the commitment value C is a kind of encrypted data, and its original plaintext information is protected. In order to make a connection between the plaintext information and the blind factor, a hash algorithm can be used to generate the blind factor. However, since the length of the hash algorithm is not infinite, and the length of the plaintext information may exceed the length of the hash algorithm, the hash of the plaintext information may have the same situation, that is, hash collision. Therefore, in the application, a characteristic vocabulary is used for hash operation to reduce hash collisions when the plaintext is hashed.

[0029] First, the frequency of each character in the key selection data is counted and recorded as S. The frequency of the i-th character is recorded as As for the uniqueness of a character, the lower the frequency of a character, the higher the uniqueness of the character. However, in order to ensure the connectivity between the character and the data, the character with the highest uniqueness is the character with a frequency of 1. Therefore, the data uniqueness of the character is calculated by the frequency of the character.

[0030] As a preferred implementation, the result of negative correlation mapping of the frequency of occurrence of each character in the key selection data is used as the data uniqueness of the character.

[0031] Optionally, the negative correlation mapping may be implemented by negative linear mapping, negative exponential mapping, or by setting adjustment parameters.

[0032] In this embodiment, the data uniqueness of the i-th character is recorded as , the expression is: ; In the formula, Indicates the number of all characters in the key selection data, Indicates the frequency of occurrence of the i-th character in the key selection data.

[0033] It should be understood that in the data processing of the commodity supply platform, lower frequency characters can ensure the privacy and security of information. The lower the frequency of characters, the higher the uniqueness of the data, which means that the blinding factors calculated by these low-frequency characters have higher uniqueness. When calculating the commitment value of plain text information, this uniqueness based on low-frequency characters can effectively reduce the risk of information leakage, thereby better meeting business needs such as privacy protection.

[0034] In other embodiments, the data uniqueness of the i-th character The expression can also be set as: , where exp() is an exponential function with the natural constant e as its base.

[0035] S3, using the data uniqueness of non-numeric characters to perform threshold segmentation on non-numeric characters, filter out some non-numeric characters, and combine them with all numeric characters, use their data uniqueness and the sequence of all their data uniqueness arranged from small to large to perform forward fusion, and use the fusion result as the data connection uniqueness of the key selection data.

[0036] Since the information on the commodity supply platform is more about commodity information data, in the process of transactions, different transaction data will have a lot of similar parts. For example, in two transactions, the types of commodities are exactly the same, but the quantity and amount of transactions are different. Therefore, through the data uniqueness of the above characters and the characters calculated, there will be a lot of similar parts, and the digital characters may have obvious differences.

[0037] Therefore, the data uniqueness of non-numeric characters is used as the input of the threshold segmentation algorithm, and the output is the segmentation threshold. The data uniqueness greater than the segmentation threshold and the data uniqueness of numeric characters are arranged in order from small to large to obtain a data uniqueness sequence. Then the data connection uniqueness of the key selection data is calculated. Among them, this embodiment adopts the Otsu threshold segmentation algorithm, which is a well-known technology and will not be repeated here.

[0038] As a preferred implementation, all element values ​​and their position sequences in the data uniqueness sequence are forwardly merged to obtain the data connection uniqueness of the key selection data.

[0039] It can be understood that forward fusion is a fusion method such as addition and multiplication between data. The specific forward fusion method is determined by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0040] In this embodiment, the sum of the products of all element values ​​in the data uniqueness sequence and their corresponding bit sequences is used as the data-related uniqueness of the key selection data. The data-related uniqueness of the key selection data is denoted as F, and the specific expression is: ; Where N represents the number of elements in the data uniqueness sequence, represents the value of the jth element in the data uniqueness sequence, and j represents the position order of the jth element in the data uniqueness sequence.

[0041] It should be understood that in the data processing of commodity supply platforms, data uniqueness is the key to ensuring information privacy and security. The higher the data uniqueness, the weaker the correlation between the data and the stronger the independence. This means that the keys generated by these highly unique data have stronger anti-attack capabilities. When encrypting and processing data, this feature based on high uniqueness can effectively resist the risk of information leakage, thereby better meeting business needs such as privacy protection and ensuring the reliability and security of data processing.

[0042] S4, using the current timestamp, constructing a data concatenation for the data-linked unique quantity as a key unique material of the blinding factor.

[0043] When calculating the commitment value of the data sent by the users of the commodity supply platform, a blinding factor is first required, and the blinding factor must be unique to ensure that when the sender encrypts the same original data multiple times, by introducing different blinding factors, it can be ensured that even if the original data is the same, the generated encrypted commitment value is different, preventing attackers from inferring the original data content through repeated encryption results and avoiding sensitive information leakage. For the above calculation of the data connection uniqueness of the key selection data, there may be a very small probability that the data connection uniqueness between different plaintext information is the same.

[0044] In order to prevent this from happening, due to the uniqueness of time, the data link unique amount of the key selection data is combined with the current timestamp to calculate the key unique material of the blinding factor.

[0045] As a preferred implementation, the data-linked unique quantity is constructed by data concatenation using the current timestamp as a key-unique material of the blinding factor.

[0046] In this embodiment, the key unique material of the blinding factor is denoted as G, ; In the formula, A unique quantity representing the data associated with the key selection data, represents the current timestamp, and cont() represents the data concatenation function. The timestamp acquisition method and the data concatenation function are both well-known technologies, and the specific process will not be repeated here.

[0047] It should be understood that in the commodity supply platform, the key unique material of the blinding factor is generated by combining the data connection unique quantity and the timestamp, which significantly enhances the security of encryption. Since the timestamp is unique when obtained at different times, the uniqueness of the timestamp ensures that the blinding factor generated each time is unique. The data concatenation function ensures that the same plaintext information generates different data after adding the timestamp, so that the same plaintext information will also generate different blinding factors, effectively preventing attackers from inferring the original data through repeated encryption results. This mechanism not only enhances the uniqueness and unpredictability of the data, but also prevents replay attacks, further improving the level of data privacy protection.

[0048] S5, obtain the hash value of the key unique material, and use the hash value as a blinding factor for cryptographic commitment generation of the plaintext information.

[0049] The key unique material calculated above is used as the input of the SHA-256 hash algorithm, the output is a hash value, and the hash value is used as a blinding factor for cryptographic commitment generation of plaintext information.

[0050] The hash algorithm in other embodiments of the present application may be other hash algorithms that have not been cracked. Among them, the calculation of the SHA-256 hash algorithm is a well-known technology, and the specific calculation process is not repeated here.

[0051] S6, after the receiver receives the plaintext information and blinding factor sent by the sender, it calculates the blinding factor according to the above method based on the received plaintext information, and compares it with the received blinding factor to determine whether the received plaintext information and blinding factor have been tampered with.

[0052] In this application, the flowchart of judging whether the received plaintext information and blinding factors have been tampered with and the subsequent operation steps is as shown in the attached figure. Figure 2 As shown, specifically: Commitment phase: During the interaction of multiple private data on the commodity supply platform, the sender will first use the hash value generated by the key unique material as the blinding factor of the Pedersen commitment, generate a commitment C from the plaintext information sent through the blinding factor, and send the commitment C to the receiver.

[0053] Opening phase: The sender sends the plaintext information and blinding factor to the receiver. After receiving the plaintext information and blinding factor sent by the sender, the receiver calculates the blinding factor according to the above method based on the received plaintext information, and compares it with the received blinding factor to determine whether the received plaintext information and blinding factor have been tampered with.

[0054] If the comparison result is inconsistent with the received blinding factor, the plaintext information and the blinding factor have been tampered with; at the same time, a message that the plaintext information and the blinding factor have been tampered with is sent to the sender; If the comparison result is consistent with the received blinding factor, the plaintext information and the blinding factor have not been tampered with; then the plaintext information is used to generate a cryptographic commitment using the blinding factor, and it is determined whether the commitment received by the recipient is consistent with the generated commitment.

[0055] When the commitment received by the receiver is consistent with the generated commitment, the private data of the interaction is consistent; When the commitment received by the receiver is inconsistent with the generated commitment, it is considered that the information sent by the sender in the commitment phase is inconsistent with the information in the opening phase, and the sender's plaintext information cannot be trusted. The sender is notified of the information inconsistency problem and is required to check it by itself.

[0056] The above technical features constitute the best implementation example of the present application, which has strong adaptability and optimal implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A multi-party private data intersection method for a commodity supply platform, characterized in that: The method comprises the following steps: S1, obtain the plain text information sent by the sender through the commodity supply platform, remove meaningless words, and use it as the key selection data; S2, the result of negative correlation mapping of the frequency of occurrence of each character in the key selection data is used as the data uniqueness of the character; S3, using the data uniqueness of non-numeric characters to perform threshold segmentation on non-numeric characters, filter out some non-numeric characters, and combine with all numeric characters, use their data uniqueness and the sequence of all their data uniqueness arranged from small to large to perform forward fusion, and use the fusion result as the data connection uniqueness of the key selection data; S4, using the current timestamp, constructing a data concatenation of the data-linked unique quantity as a key unique material of the blinding factor; S5, obtaining a hash value of the key unique material, and using the hash value as a blinding factor for cryptographic commitment generation of the plaintext information; S6, after the receiver receives the plaintext information and blinding factor sent by the sender, it calculates the blinding factor according to the above method based on the received plaintext information, and compares it with the received blinding factor to determine whether the received plaintext information and blinding factor have been tampered with.

2. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 1, characterized in that: The method of removing meaningless words adopts data bleaching method.

3. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 1, characterized in that: The method for calculating the data uniqueness is further determined as follows: record the data uniqueness of the i-th character , ; In the formula, Indicates the number of all characters in the key selection data, Indicates the frequency of occurrence of the i-th character in the key selection data.

4. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 1, characterized in that: The threshold segmentation algorithm further adopts the Otsu threshold segmentation algorithm.

5. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 1, characterized in that: The method for filtering some non-numeric characters is: non-numeric characters whose data uniqueness is greater than the segmentation threshold are used as the filtered out part of the non-numeric characters.

6. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 1, characterized in that: The calculation method of the data connection uniqueness of the key selection data is further determined as: the sum of the products of all element values ​​in the data uniqueness sequence and their corresponding bit sequences.

7. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 1, characterized in that: The hash value calculation method further adopts the SHA-256 hash algorithm.

8. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 1, characterized in that: The cryptographic commitment method adopts Pedersen commitment.

9. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 1, characterized in that: The method for determining whether the received plaintext information and blinding factors have been tampered with is: If the comparison result is inconsistent with the received blinding factor, the plaintext information and the blinding factor have been tampered with; At the same time, the plaintext information and the message that the blinding factor has been tampered with are sent to the sender; If the comparison result is consistent with the received blinding factor, the plaintext information and the blinding factor have not been tampered with; then the plaintext information is used to generate a cryptographic commitment using the blinding factor, and it is determined whether the commitment received by the recipient is consistent with the generated commitment.

10. A method for intersecting multi-party private data for a commodity supply platform as claimed in claim 9, characterized in that: When the commitment received by the receiver is consistent with the generated commitment, the interactive private data is consistent; when the commitment received by the receiver is inconsistent with the generated commitment, it is considered that the information sent by the sender in the commitment phase is inconsistent with the information sent in the opening phase, and the sender's plaintext information cannot be trusted. The sender is notified of the information inconsistency problem and is required to check it by itself.

Images