A multi-party private data intersection method for commodity supply platform

Abstract

The present application relates to the field of privacy data interaction technology, and specifically to a multi-party privacy data intersection method for a commodity supply platform, the method comprising: using the frequency of occurrence of each character in the key selection data to determine the data uniqueness of each character, and calculating the data connection uniqueness of the key selection data; and using the current timestamp to perform data concatenation construction on the data connection uniqueness, and then calculating the hash value as a blinding factor for cryptographic commitment generation of the plaintext information; the receiver uses the plaintext information and blinding factor received from the sender to determine whether the received plaintext information and blinding factor have been tampered with. The present application aims to calculate the blinding factor through the plaintext information itself, avoiding the verification complexity caused by the random generation of blinding factors in traditional methods, so that the receiver can directly discover whether the data has been tampered with without the need for multi-party confirmation, significantly improving the efficiency of data verification and saving the user's time and computing resources.

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 intersection method for a commodity supply platform. Background Art

[0002] The application of privacy-preserving technologies is crucial for multi-party private data interactions on commodity supply platforms. With the rapid development of the data factor market, privacy-preserving computing technologies such as zero-knowledge proofs, homomorphic encryption, and secure multi-party computation (SMC) have become key tools for achieving data privacy protection. Zero-knowledge proofs allow one party to prove the truth of a statement without disclosing any information, and are widely used in financial services and private blockchain transactions. Homomorphic encryption supports direct computation on encrypted data, obtaining encrypted results without decryption, making it suitable for scenarios such as cloud computing and data mining. Furthermore, secure multi-party computation allows multiple participants to jointly complete computational tasks without disclosing their own data, and is applicable in areas such as electronic elections, threshold signatures, and electronic auctions. The combination of these technologies provides commodity supply platforms with "interoperable, verifiable, and invisible" privacy protection, meeting the need for secure sharing of commercially sensitive data within the supply chain.

[0003] To ensure the confidentiality and binding nature of the sender's information during multi-party private data exchange, traditional methods typically use cryptographic commitment schemes (Pederse commitments) to encrypt and sign private data. However, these methods generate blinding factors using random methods. If the plaintext and blinding factors are tampered with during transmission, it is impossible to directly determine whether the information has been tampered with. The commitment value must be calculated, requiring multiple parties to confirm whether the information has been tampered with. This wastes a significant amount of user time and makes Pedersen commitments 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 present application discloses a method for intersecting private data of multiple parties on a commodity supply platform using the following technical solutions:

[0006] One 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:

[0007] S1, obtains the plaintext information sent by the sender through the commodity supply platform, removes meaningless words, and uses the data as the key selection data;

[0008] S2, the result of negative correlation mapping of the frequency of each character in the key selection data is used as the data uniqueness of the character;

[0009] 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. Using their data uniqueness and the order of all their data uniqueness arranged from small to large, forward fusion is performed, and the fusion result is used as the data connection uniqueness of the key selection data;

[0010] S4, using the current timestamp, constructing a data concatenation of the data-linked unique quantity as a key unique material of the blinding factor;

[0011] 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;

[0012] S6. After the receiver receives the plaintext information and blinding factor from 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.

[0013] Preferably, the method for removing meaningless words adopts a data bleaching method.

[0014] Preferably, the method for calculating the data uniqueness is further determined as follows: the data uniqueness of the i-th character is recorded as , Where, 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.

[0015] Preferably, the threshold segmentation algorithm further adopts Otsu threshold segmentation algorithm.

[0016] 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.

[0017] 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.

[0018] Preferably, the hash value calculation method further adopts the SHA-256 hash algorithm.

[0019] Preferably, the cryptographic commitment method adopts Pedersen commitment.

[0020] Preferably, the method for determining whether the received plaintext information and blinding factor have been tampered with is:

[0021] If the comparison result with the received blinding factor is inconsistent, 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;

[0022] 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 cryptographically committed using the blinding factor, and it is determined whether the commitment received by the recipient is consistent with the generated commitment.

[0023] 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. The sender is notified of the information inconsistency problem and is required to check it on its own.

[0024] This application has at least the following beneficial effects:

[0025] 1. This invention calculates the blinding factor from the plaintext itself, avoiding the verification complexity caused by the random generation of blinding factors in traditional methods. When verifying data, the recipient can directly determine whether the data has been tampered with without the need for multiple confirmations. This significantly improves data verification efficiency and saves users time and computing resources.

[0026] 2. This invention utilizes the uniqueness of low-frequency characters to generate blinding factors, reducing the possibility of hash collisions. Furthermore, the unique key material used to generate the blinding factors, combined with the timestamp, ensures the uniqueness of each encryption. This mechanism not only enhances data uniqueness and unpredictability but also effectively prevents attackers from deducing the original data through repeated encryption results, further enhancing data privacy protection.

[0027] 3. This invention achieves efficient and secure multi-party private data exchange through an optimized commitment generation and verification mechanism. The sender generates an encrypted commitment value during the commitment phase, and the receiver verifies data consistency during the opening phase. This "interoperable, verifiable, and invisible" privacy protection approach meets the need for secure sharing of commercially sensitive data within the supply chain and provides a reliable technical guarantee for multi-party private data exchange on product supply platforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0029] Figure 1 This is a flowchart of a method for intersecting multi-party private data on a commodity supply platform provided in this application;

[0030] Figure 2 This application provides a flowchart for determining whether the received plaintext information and blinding factors have been tampered with and its subsequent operation steps. DETAILED DESCRIPTION

[0031] To further illustrate the technical means and effects employed by this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method for obtaining multi-party private data intersection for a commodity supply platform, including its specific implementation, structure, features, and effects. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0032] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0033] The following describes in detail a specific solution of a multi-party private data intersection method for a commodity supply platform provided by this application with reference to the accompanying drawings.

[0034] An embodiment of the present application provides a method for intersecting multi-party private data on a commodity supply platform.

[0035] During multi-party private data exchange, some recipients may not be able to access the information in advance. To ensure that the information is not tampered with by the sender after the recipient knows it, it is necessary to conceal the information sent by the sender and send a commitment value. When the sender is required to send plaintext information and blinding factors, in order to prevent the plaintext information and blinding factors from being tampered with, the present application calculates the blinding factors using the plaintext information itself, ensuring that the recipient can directly detect whether the data has been tampered with when verifying the data.

[0036] Specifically, the following method for finding the intersection of multiple private data for a commodity supply platform is provided. Figure 1 , the method comprises the following steps:

[0037] S1, obtains the plaintext information sent by the sender through the commodity supply platform, removes meaningless words, and uses it as the key selection data.

[0038] First, obtain the plaintext information sent by the sender through the commodity supply platform. Then, remove the meaningless characters in the plaintext information through the data bleaching method, such as: "de", ".", ",", "le". The plaintext information after data bleaching is called the key selection data. The calculation of data bleaching is a well-known technology, and the specific calculation process will not be elaborated here.

[0039] S2, use the result of the negative correlation mapping of the frequency of each character in the key selection data as the data uniqueness of this character.

[0040] When generating the blind factor for the data sent by the users of the commodity supply platform, since the data sent by the sender for the first time is the commitment value C, and the commitment value C is an encrypted data, and its original plaintext information is in a protected state. In order to establish a connection between the plaintext information and the blind factor, the 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, it may cause the same hash for the plaintext information, that is, hash collision. Therefore, in this application, characteristic vocabulary is used for hash operation to reduce the hash collision when the plaintext is performing hash operation.

[0041] First, by counting the frequency of each character in the key selection data, record it as S. The frequency of the i-th character is recorded as . For the uniqueness of the character, when the frequency of the character appears lower, the uniqueness of the character is higher. However, in order to ensure the connection between the character and the data, therefore, the character with the highest uniqueness is the character with a frequency of 1. Thus, calculate the data uniqueness of the character through the frequency of the character.

[0042] As a preferred implementation, use the result of the negative correlation mapping of the frequency of each character in the key selection data as the data uniqueness of this character.

[0043] Optionally, the negative correlation mapping can be implemented by methods such as negative linear mapping, negative exponential mapping or setting adjustment parameters.

[0044] In this embodiment, record the data uniqueness of the i-th character as , and the expression is: ; in the formula, represents the total number of all characters in the key selection data, represents the frequency of the i-th character in the key selection data.

[0045] It should be understood that in data processing on product supply platforms, lower-frequency characters can ensure information privacy and security. The lower the frequency of a character, the higher the uniqueness of the data, which means that the blinding factors generated by these low-frequency characters have higher uniqueness. When calculating commitment values ​​for plaintext information, this uniqueness based on low-frequency characters can effectively reduce the risk of information leakage, thereby better meeting business requirements such as privacy protection.

[0046] 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.

[0047] 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, using their data uniqueness and the order 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.

[0048] Because commodity supply platforms primarily contain product data, different transaction data can have significant similarities during transactions. For example, two transactions may involve the exact same product type, but differ only in quantity and amount. Therefore, based on the data uniqueness of the aforementioned characters and their calculated counterparts, there will be significant similarities, while numeric characters may be significantly different.

[0049] 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 ascending order to obtain a data uniqueness sequence. The data connection uniqueness of the key selection data is then calculated. This embodiment uses the Otsu threshold segmentation algorithm, which is a well-known technique and will not be described in detail.

[0050] As a preferred implementation, all element values ​​and their position sequences in the data uniqueness sequence are forward fused to obtain the data connection uniqueness of the key selection data.

[0051] 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.

[0052] 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-linked uniqueness of the key selection data. The data-linked 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 bit order of the jth element in the data uniqueness sequence.

[0053] It should be understood that in data processing on product supply platforms, data uniqueness is key to ensuring information privacy and security. Higher data uniqueness indicates weaker correlations and greater independence between data. This means that keys generated from this highly unique data are more resistant to attacks. This high uniqueness effectively mitigates the risk of information leakage during data encryption and processing, thereby better meeting business requirements such as privacy protection and ensuring the reliability and security of data processing.

[0054] S4, using the current timestamp, constructing a data concatenation for the data-linked unique quantity as a key unique material of the blinding factor.

[0055] When calculating the commitment value for data sent by users of a product supply platform, a blinding factor is first required. This blinding factor must be unique. This ensures that when the sender encrypts the same original data multiple times, the generated encrypted commitment value will be different, even if the original data is the same. This prevents attackers from deducing the original data content through repeated encryption results and avoids the leakage of sensitive information. Regarding the data-linked uniqueness of the key selection data calculated above, there may be a very small probability that the data-linked uniqueness of different plaintext messages is the same.

[0056] In order to prevent this from happening, due to the uniqueness of time, the key selection data is combined with the unique amount of data and the current timestamp to calculate the key unique material of the blinding factor.

[0057] 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.

[0058] In this embodiment, the key unique material of the blinding factor is recorded as G, Where, A unique quantity representing the data associated with the key selection data. Represents the current timestamp, and cont() represents a 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.

[0059] It should be understood that within the commodity supply platform, the unique key material for the blinding factor is generated by combining a unique data link and a timestamp, significantly enhancing encryption security. Since timestamps are unique when acquired at different times, this uniqueness ensures that each generated blinding factor is unique. The data concatenation function ensures that the same plaintext information, when timestamped, generates different data, resulting in different blinding factors for the same plaintext. This effectively prevents attackers from deducing the original data through repeated encryption results. This mechanism not only enhances data uniqueness and unpredictability, but also prevents replay attacks, further enhancing data privacy.

[0060] S5, obtain the hash value of the key unique material, and use the hash value as a blinding factor for generating a cryptographic commitment for the plaintext information.

[0061] The key unique material calculated above is used as input to the SHA-256 hash algorithm, the output is a hash value, and the hash value is used as a blinding factor to generate a cryptographic commitment for the plaintext information.

[0062] The hash algorithm in other embodiments of the present application can 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.

[0063] S6. After the receiver receives the plaintext information and blinding factor from 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.

[0064] 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 shown in the attached figure. Figure 2 As shown, specifically:

[0065] Commitment phase: During the interaction of private data between multiple parties on the product supply platform, the sender first uses the hash value generated by the key's unique material as the blinding factor of the Pedersen commitment. The blinding factor is used to generate a commitment C from the plaintext information sent, and the commitment C is sent to the receiver.

[0066] 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.

[0067] If the comparison result with the received blinding factor is inconsistent, 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;

[0068] 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 cryptographically committed using the blinding factor, and it is determined whether the commitment received by the recipient is consistent with the generated commitment.

[0069] When the commitment received by the receiver is consistent with the generated commitment, the private data of the interaction is consistent;

[0070] 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.

[0071] The above technical features constitute the best embodiment of the present application, which has strong adaptability and optimal implementation effect. Non-essential technical features can be added or removed 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, obtains the plaintext information sent by the sender through the commodity supply platform, removes meaningless words, and uses the data as the key selection data; S2, the result of negative correlation mapping of the frequency 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 them with all numeric characters. Using their data uniqueness and the order of all their data uniqueness arranged from small to large, forward fusion is performed, and the fusion result is used 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 receiving the plaintext information and blinding factor from 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. The method for calculating the data uniqueness is further determined as follows: the data uniqueness of the i-th character is recorded as , Where, 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.

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

3. The method for intersecting multi-party private data on a commodity supply platform according to claim 1, wherein: The threshold segmentation algorithm further adopts the Otsu threshold segmentation algorithm.

4. The method for intersecting multi-party private data on a commodity supply platform according to claim 1, wherein: The method for filtering some non-numeric characters is: non-numeric characters whose data uniqueness is greater than the segmentation threshold are regarded as the filtered out non-numeric characters.

5. The method for intersecting multi-party private data on a commodity supply platform according to claim 1, wherein: 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 ​​and their corresponding bit sequences in the data uniqueness sequence.

6. The method for intersecting multi-party private data on a commodity supply platform according to claim 1, wherein: The hash value calculation method further adopts the SHA-256 hash algorithm.

7. The method for intersecting multi-party private data on a commodity supply platform according to claim 1, wherein: The cryptographic commitment method adopts Pedersen commitment.

8. The method for intersecting multi-party private data on a commodity supply platform according to claim 1, wherein: 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 whose 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 cryptographically committed using the blinding factor, and it is determined whether the commitment received by the recipient is consistent with the generated commitment.

9. A method for intersecting multi-party private data on a commodity supply platform as claimed in claim 8, characterized in that: 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 sent in the opening phase, and the sender's plaintext information cannot be trusted. The sender is notified of the information inconsistency problem and needs to check it by itself.

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