User privacy data encryption protection method for commodity supply chain platform

Abstract

The invention relates to the technical field of data encryption, in particular to a user privacy data encryption protection method for a commodity supply chain platform, and the method comprises the steps: analyzing a membership degree vector of each user according to user privacy data obtained from the commodity supply chain platform; constructing a mapping function for each type of privacy data of each user in each clustering cluster according to the membership degree between the user and each clustering cluster and the difference between each user and other users in each clustering cluster in each type of privacy data, and combining a secret key obtained by encrypting the privacy data of all the users in each clustering cluster, so as to construct a mapping function for each type of privacy data of each user in each clustering cluster; performing blind mapping on each type of privacy data of the user; and encrypting all the blinded user privacy data in each cluster. According to the invention, under the condition that excessive performance is not sacrificed, finer-grained access control and a stronger data protection mechanism are realized.

Description

Technical Field

[0001] The present application relates to the field of data encryption technology, and in particular to a method for encrypting and protecting user privacy data of a commodity supply chain platform. Background Art

[0002] With the rapid development of e-commerce and global trade, commodity supply chain platforms play a vital role in the modern economy. These platforms not only connect manufacturers, suppliers, distributors and consumers, but also process a large amount of user privacy data. Existing supply chain platforms usually use layered encryption to uniformly encrypt the same type of data or user data with high similarity, while ensuring the security of platform data, reducing platform overhead and improving operational efficiency.

[0003] However, these methods usually encrypt the same type of user data or data with high similarity, which improves efficiency while sacrificing some security, reduces the system's fine-grained access control capabilities, and increases the possibility of data leakage. In theory, encrypting each user separately is relatively secure, but it will significantly increase the burden on the system. At the same time, maintaining a large key space will also increase storage overhead and affect the overall performance of the system.

[0004] Existing solutions find it difficult to achieve more fine-grained access control and stronger data protection mechanisms without sacrificing too much performance, which to some extent restricts the overall security of the supply chain platform. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a method for encrypting and protecting user privacy data of a commodity supply chain platform to solve the existing problems.

[0006] The user privacy data encryption protection method of a commodity supply chain platform of the present application adopts the following technical solution: An embodiment of the present application provides a method for encrypting and protecting user privacy data of a commodity supply chain platform, the method comprising the following steps: S1, obtain user privacy data of the commodity supply chain platform; S2, clustering users using fuzzy clustering algorithm based on the feature vectors of user transaction records of all users in the user privacy data, and outputting the membership vector of each user; S3, determining a blinding coefficient vector for each category of private data of each user in each cluster according to the membership between the user and each cluster and the difference between each category of private data of each user and other users in the cluster; S4, combining the key obtained by encrypting the private data of all users in each cluster, the membership vector of each user and the blinding coefficient vector, constructing a mapping function for each type of private data of each user in each cluster, and performing blind mapping on each type of private data of the user; S5, encrypt all blinded user privacy data in each cluster.

[0007] Preferably, the user privacy data includes three types of privacy data: user basic identity information, user transaction records, and user identity sensitive information.

[0008] Preferably, the basic user identity information includes name, gender, age, and contact information; the sensitive user identity information includes personal ID number and bank card number; and the user transaction record includes transaction amount, transaction commodity category, and identities of both parties to the transaction.

[0009] Preferably, the feature vector of the user transaction record is composed of all historical transaction amounts, transaction commodity categories, and identities of both transaction parties of each user.

[0010] Preferably, the clustering distance when the fuzzy clustering algorithm is adopted is the distance between feature vectors.

[0011] Preferably, the method for determining the blinding coefficient vector of each type of privacy data of each user in each cluster is: The first Users in the cluster The privacy data of The blinding coefficient vector of the class privacy data is recorded as , ; in, For users Belong to The membership degree of the clusters, For users The sum of the elements in the membership vector of For the Users in clusters With the rest of the users The difference vector between the class private data.

[0012] Preferably, ,in For the The number of attributes in the class-private data, Is a user With The remaining users in the cluster The mean of the first attribute data difference measure in the private data, , Corresponding to the second and The mean of the difference measures of the attribute data.

[0013] Preferably, the method for constructing a mapping function for each type of privacy data of each user in each cluster is: The first Users in the cluster The privacy data of The mapping function of class privacy data is recorded as , ; in, For the Users in the cluster The privacy data of The blinding coefficient vector of the private data class, Is a user No. A column vector consisting of class-private data, Is a user The transpose of the membership vector of Is a user No. The dimension of the column vector composed of class-private data, Is a user No. The first column vector of the private data Dimensional data, It is The key obtained by encrypting all user privacy data in a cluster.

[0014] Preferably, the membership vector and the blinding coefficient vector are continuously updated as the user privacy data in the supply chain platform changes.

[0015] Preferably, when decrypting user privacy data, it is necessary to input the decryption key, membership vector and blinding coefficient vector of the corresponding cluster cluster when uniformly encrypted, first use the decryption key to decrypt to obtain the blinded user privacy data, and then use the mapping function to restore the blinded user privacy data to the original user privacy data.

[0016] This application has at least the following beneficial effects: Firstly, the present application can simultaneously perform privacy data of all users through fuzzy clustering and membership calculation, avoiding the overhead of generating keys for each user and the storage burden caused by managing and updating keys, thereby ensuring the operation efficiency of the system; secondly, this protection method also has its unique advantage in timeliness. As the privacy data of users in the system changes, the membership vector and the blinding coefficient vector also change in real time, so the membership vector and the blinding coefficient vector obtained by the user can only play a role within a limited time. After the fuzzy clustering is performed again, the membership vector and the blinding coefficient vector will also be updated, thereby realizing the functions of revoking authority and updating keys; the blinding coefficient vector can be adaptively adjusted according to the membership between the user and each clustering cluster, and the degree of blinding can be controlled to further reduce the probability of successful attack by the adversary; finally, the membership vector of each user can be uniformly obtained through fuzzy clustering, avoiding the overhead caused by separate processing for each user, and also avoiding the huge key space management when encrypting each user separately. In this way, the system does not need to manage and update keys, and only needs to use mapping functions to achieve similar functions, thereby reducing the storage overhead of the system. In summary, this application achieves finer-grained access control and stronger data protection mechanism without sacrificing too much performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] Figure 1 A flowchart of a method for encrypting and protecting user privacy data on a commodity supply chain platform provided in this application. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following is a detailed description of the user privacy data encryption protection method of a commodity supply chain platform proposed in this application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] 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.

[0021] The following is a detailed description of a method for encrypting and protecting user privacy data of a commodity supply chain platform provided by the present application in conjunction with the accompanying drawings.

[0022] An embodiment of the present application provides a method for encrypting and protecting user privacy data of a commodity supply chain platform.

[0023] Specifically, the following method for encrypting and protecting user privacy data of a commodity supply chain platform is provided. Figure 1 , the method comprises the following steps: S1, obtain user privacy data of the commodity supply chain platform.

[0024] The commodity supply chain platform contains various types of private data of users, which can be roughly divided into basic user identity information, user transaction records, and user identity-sensitive information. Basic user identity information includes but is not limited to name, gender, age, and contact information. This type of information can be directly quantified in text form. For example, the name can be converted into a string, and different genders can be represented by 0 or 1 respectively. The specific conversion method is a well-known method in the field of data processing and is not specifically limited, as long as various data can be converted into quantifiable numerical form.

[0025] User identity sensitive information includes but is not limited to personal ID number and bank card number; user transaction records include the user's transaction amount, transaction commodity category and the identities of both parties. Since the user's basic identity information and identity sensitive information are unique, they cannot be used to classify users in the supply chain platform; however, user transaction records can be used to classify users.

[0026] In the user transaction record, the user's transaction amount is saved in numerical form, and the user's transaction commodity category is quantified using a one-hot encoding method, and different commodity category codes can be assigned to each commodity category in turn; The transaction amount of a user is sequenced according to the size of the corresponding commodity category code; Secondly, the identities of the two parties to the transaction include but are not limited to suppliers, supplier accounts, sellers, and seller accounts. A supplier and its account together constitute a supplier vector, and a seller and its account together constitute a seller vector. All supplier vectors in a user's transaction record constitute the user's supplier sequence in order of account size, and all seller vectors constitute the user's seller sequence in order of account size.

[0027] S2, based on the feature vectors of user transaction records of all users in the user privacy data, uses a fuzzy clustering algorithm to cluster users and outputs the membership vector of each user.

[0028] Large supply chain platforms have a large amount of user privacy data, and these privacy data are highly real-time, that is, as transactions proceed, the content of each user's privacy data is constantly changing. In order to maintain data privacy, the supply chain platform also needs to frequently encrypt user data. Therefore, supply chain platforms usually use layered encryption to encrypt different types of data separately. At the same time, in order to ensure data consistency and efficient data management, they will also choose to uniformly encrypt and manage data that has certain relationships or is similar, thereby improving system efficiency. However, this leads to a weakening of fine-grainedness. When a user obtains a permission, he actually obtains access rights to a group of data, which violates the principle of least privilege and poses a threat to system information security. Therefore, it is necessary to further enhance fine-grained permission control on the basis of the current encryption management method to improve the security of the supply chain platform.

[0029] When encrypting the privacy data of the supply chain platform, it is necessary to first classify the users and manage the users with certain correlation or similarity in a unified manner, so as to improve the efficiency of the supply chain platform. However, the traditional data classification method has certain limitations. When classifying users, due to the variability of user data, it will further threaten the security of the system. For example, a user may be a supplier and a seller at the same time, so he is associated with his seller and his supplier at the same time. If all the data related to the user are divided together, the corresponding classification will be too large, and each user in the classification will be able to obtain a large amount of data that is irrelevant or weakly related to himself, which is undoubtedly a serious security vulnerability; but if the user is only fixed to a certain category, users in other categories may not be associated with the user, which will also cause certain loopholes in data management.

[0030] The unique hot encoding of the transaction commodity category, transaction amount sequence, supplier sequence, and seller sequence in each user's user transaction record together constitute a feature vector of the user transaction record of a user.

[0031] All users' feature vectors and the set number of clusters and fuzziness factor As input, the fuzzy clustering algorithm is used to calculate the membership degree and output the membership matrix, where the number of clusters is It can be set according to the needs of the system, or it can be determined using the elbow method. When the number of clusters is greater, the burden on the system is relatively greater; the fuzzy factor The recommended value range is [1,2]. As the fuzzy clustering algorithm and the elbow method are well-known technologies, the specific details are not repeated here. In different implementations, the fuzzy clustering algorithm can select fuzzy C-means clustering, fuzzy K-means clustering, and Gustafson-Kessel (GK) clustering.

[0032] It should be noted that the one-hot encoding of the transaction commodity category is a string of binary data. The size of the data itself does not have any meaning, and the 0 or 1 of each bit of data indicates whether there is a corresponding transaction category, which is meaningful. Therefore, in different implementations, the one-hot encoding of the transaction commodity category needs to use the edit distance or the Hamming distance when calculating the distance; the supplier sequence and the seller sequence may have different lengths, so the edit distance or the DTW distance can be used in different implementations.

[0033] The distances calculated by various distance formulas are different. In this embodiment, the distances between the data of the same dimension in the two user feature vectors are calculated respectively, and the calculated distances are normalized. Finally, the sum of all normalized distances is the distance formula of fuzzy clustering. Edit distance, Hamming distance, DTW distance and normalization are well-known technologies and will not be described in detail.

[0034] S3, determining the blinding coefficient vector of each category of privacy data of each user in each cluster according to the membership between the user and each cluster and the difference between each category of privacy data between each user and other users in the cluster.

[0035] After obtaining the membership matrix according to step S2, the membership vector of each user is obtained, which measures the membership of each user's information to each cluster, and as the user information in the entire user space changes, the membership will also change accordingly. That is, the corresponding membership vector is time-sensitive; and the adversary is relatively safe if he has not cracked all the clusters to which a user belongs and all the user privacy data in these clusters.

[0036] Therefore, the membership vector of each user can be used to blind the user data, thereby further ensuring the security of the user's privacy data. At the same time, the membership vector can be used to achieve more fine-grained access control.

[0037] Secondly, since the membership vector of each user indicates the degree of correlation between the user data and the elements in each cluster, when the membership between the user and a cluster is small, it means that the correlation between the user and other elements in the cluster is also small. Therefore, when blinding, a smaller blinding coefficient can be used to control the difference between the user data before and after blinding; when the membership between the user and a cluster is large, the correlation between the user data and other elements in the cluster is large. At this time, when blinding, if a smaller blinding coefficient is still used, the difference before and after data blinding may be small, thereby increasing the probability that the adversary can obtain the user data characteristics or privacy information by cracking and analyzing the data characteristics of other elements in the cluster, which increases the possibility of privacy leakage to a certain extent.

[0038] Therefore, it is necessary to set a corresponding blinding coefficient vector for each type of privacy data of each user in each cluster according to different membership situations.

[0039] In this embodiment, the calculation method of the blinding coefficient vector is as follows: : For the Users in the cluster The privacy data of The blinding coefficient vector of the private data class, For users Belong to The membership degree of the clusters, For users The sum of the elements in the membership vector of For the Users in clusters With the rest of the users The difference vector between the class private data.

[0040] In this embodiment, ,in For the The number of attributes in the class-private data, Is a user With The remaining users in the cluster The mean of the first attribute data difference measure in the privacy-like data, and so on. , Corresponding to the second and The mean of the difference measures of the attribute data.

[0041] It should be noted that if the corresponding attribute data is in numerical form, the difference measure is the reciprocal of the normalized difference; if the corresponding data is in vector form, the difference measure is the cosine similarity.

[0042] It can be understood that when the user's membership in a cluster is higher, the corresponding data similarity is greater, and thus a larger blinding coefficient is required to ensure that the data difference before and after blinding is greater; similarly, for each data in a category of data, the smaller the difference with other elements in the same cluster, the larger the blinding coefficient is required to ensure that the difference before and after blinding is greater, so the corresponding blinding coefficient is larger.

[0043] S4, combining the key obtained by encrypting the private data of all users in each cluster, the membership vector of each user and the blinding coefficient vector, constructing a mapping function for each type of private data of each user in each cluster, and performing blind mapping on each type of private data of the user.

[0044] In this embodiment, the mapping function is constructed as follows: : For the Users in the cluster The privacy data of Mapping function for class private data, For the Users in the cluster The privacy data of The blinding coefficient vector of the private data class, Is a user No. A column vector consisting of class-private data, Is a user The transpose of the membership vector of Is a user No. The dimension of the column vector composed of class-private data, Is a user No. The first column vector of the private data Dimensional data, It is The key obtained by encrypting all user privacy data in a cluster.

[0045] Among them, The key obtained by encrypting the privacy data of all users in a cluster can be obtained through the symmetric encryption algorithm AES, and the specific encryption algorithm can be selected by the implementer.

[0046] It should be noted that the blinding coefficient vector The dimension is equal to , after the blind coefficient vector is generated, it is sent to the user together with the membership vector The types of privacy data in the user privacy data in this embodiment include user basic identity information, user transaction records, and user identity sensitive information, that is, .

[0047] It can be understood that by using the mapping function constructed above, users can blind the original privacy data according to the membership vector. In the subsequent decryption, in addition to the unified encryption key, the user's own membership vector is also required, thereby improving the fine-grained permission control capability of the supply chain platform on the basis of traditional hierarchical encryption; the key It ensures that only user mapping data within the same cluster can be obtained during subsequent decryption. The membership vector ensures that only data corresponding to users in the cluster can be obtained, realizing fine-grained permission control. The blinding coefficient vector further enhances the fine-grained access control capability and can control the specific type of data obtained.

[0048] According to the mapping function constructed above, each type of privacy data of each user in the supply chain platform is blinded to obtain the blinded user privacy data.

[0049] S5, encrypt all blinded user privacy data in each cluster.

[0050] After obtaining the blinded user privacy data, all the blinded user privacy data in each cluster are uniformly encrypted according to the result of fuzzy clustering.

[0051] It should be noted that encryption is for data mapped by the mapping function, that is, blinded user privacy data. In different implementations, encryption can use any cryptographic algorithm.

[0052] In addition, when the user decrypts, he needs to input the decryption key, membership vector and blinding coefficient vector of the corresponding cluster cluster when it is uniformly encrypted. First, the decryption key is used to decrypt to obtain the blinded user privacy data, and then the mapping function is used to restore the blinded user privacy data to the original user privacy data, thereby realizing the acquisition of user privacy data.

[0053] At the same time, users can also delegate their authority to others by simply transferring the membership vector and blinding coefficient vector.

[0054] As the user privacy data in the supply chain platform changes, the membership vector and the blinding coefficient vector are constantly updated, and the supply chain platform does not need to perform any storage management updates. Therefore, when delegating permissions, there is no need to consider subsequent permission revocation.

[0055] 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 needs of different situations.

Claims

1. A method for encrypting and protecting user privacy data of a commodity supply chain platform, characterized in that: The method comprises the following steps: S1, obtain user privacy data of the commodity supply chain platform; S2, clustering users using fuzzy clustering algorithm based on the feature vectors of user transaction records of all users in the user privacy data, and outputting the membership vector of each user; S3, determining a blinding coefficient vector for each category of private data of each user in each cluster according to the membership between the user and each cluster and the difference between each category of private data of each user and other users in the cluster; S4, combining the key obtained by encrypting the private data of all users in each cluster, the membership vector of each user and the blinding coefficient vector, constructing a mapping function for each type of private data of each user in each cluster, and performing blind mapping on each type of private data of the user; S5, encrypt all blinded user privacy data in each cluster.

2. A method for encrypting and protecting user privacy data of a commodity supply chain platform as claimed in claim 1, characterized in that: The user privacy data includes three types of privacy data: user basic identity information, user transaction records, and user identity sensitive information.

3. A method for encrypting and protecting user privacy data of a commodity supply chain platform as claimed in claim 2, characterized in that: The user's basic identity information includes name, gender, age, and contact information; the user's sensitive identity information includes personal ID number and bank card number; the user's transaction record includes transaction amount, transaction commodity category, and the identities of both parties to the transaction.

4. A method for encrypting and protecting user privacy data of a commodity supply chain platform as claimed in claim 3, characterized in that: The feature vector of the user transaction record is composed of all historical transaction amounts, transaction commodity categories, and identities of both transaction parties of each user.

5. The method for encrypting and protecting user privacy data of a commodity supply chain platform according to claim 1, characterized in that: The clustering distance when the fuzzy clustering algorithm is adopted is the distance between the feature vectors.

6. A method for encrypting and protecting user privacy data of a commodity supply chain platform as claimed in claim 1, characterized in that: The method for determining the blinding coefficient vector of each type of privacy data of each user in each cluster is as follows: The first Users in the cluster The privacy data of The blinding coefficient vector of the class privacy data is recorded as , ; in, For users Belong to The membership degree of the clusters, For users The sum of the elements in the membership vector of For the Users in clusters With the rest of the users The difference vector between the class private data.

7. A method for encrypting and protecting user privacy data of a commodity supply chain platform as claimed in claim 6, characterized in that: ,in For the The number of attributes in the class-private data, Is a user With The remaining users in the cluster The mean of the first attribute data difference measure in the private data, , Corresponding to the second and The mean of the difference measures of the attribute data.

8. The method for encrypting and protecting user privacy data of a commodity supply chain platform according to claim 1, characterized in that: The method for constructing a mapping function for each type of privacy data of each user in each cluster is: The first Users in the cluster The privacy data of The mapping function of class privacy data is recorded as , ; in, For the Users in the cluster The privacy data of The blinding coefficient vector of the private data class, Is a user No. A column vector consisting of class-private data, Is a user The transpose of the membership vector of Is a user No. The dimension of the column vector composed of class-private data, Is a user No. The first column vector of the private data Dimensional data, It is The key obtained by encrypting all user privacy data in a cluster.

9. The method for encrypting and protecting user privacy data of a commodity supply chain platform according to claim 1, characterized in that: The membership vector and the blinding coefficient vector are continuously updated as the user privacy data in the supply chain platform changes.

10. The method for encrypting and protecting user privacy data of a commodity supply chain platform according to claim 1, characterized in that: When decrypting user privacy data, it is necessary to input the decryption key, membership vector and blinding coefficient vector of the corresponding cluster cluster when it is uniformly encrypted. First, use the decryption key to decrypt and obtain the blinded user privacy data, and then use the mapping function to restore the blinded user privacy data to the original user privacy data.

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