User data protection method for trans-provincial and trans-regional power transaction platform

Abstract

The invention relates to the technical field of data processing, in particular to a user data protection method for a trans-provincial and trans-regional power transaction platform, and the method comprises the steps: analyzing the relation between transaction attributes and transaction behaviors through user data in the trans-provincial and trans-regional power transaction platform, and combining the difference between the transaction attributes of different users, constructing transaction privacy sensitivity; constructing a user transaction association degree of each type of power transaction data of each user based on an association relationship between the same type of power transaction data of different users in the power transaction platform; according to the invention, the necessary transaction attribute of each user is obtained based on the transaction privacy sensitivity and the user transaction association degree, and the user data is encrypted based on the necessary transaction attribute in combination with a plurality of encryption algorithms, so that the leakage risk of the user data in the trans-provincial and trans-regional power transaction platform when the user data is accessed by adopting a single key generation strategy is reduced; and the security of user data access is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a method for protecting user data for an inter-provincial and inter-regional power trading platform. Background Art

[0002] The inter-provincial and inter-regional power trading platform usually refers to a platform that provides services for power trading between regional or provincial power grids formed by various operating entities. During the period when users complete power trading according to power demand through the inter-provincial and inter-regional power trading platform, the inter-provincial and inter-regional power trading platform usually retains user data for the entire period, including: transaction contracts, user information, transaction amounts, user units, authorization documents, etc. These user data not only help the inter-provincial and inter-regional power trading platform to quickly and accurately dispatch and trade power, but also involve the protection of user privacy information.

[0003] Since a large number of different types of user data are stored in the inter-provincial and inter-regional power trading platform, and a large number of power transactions are carried out, it is necessary to implement data encryption protection without leaking information. Due to the different sensitivity and importance of different types of user data on the inter-provincial and inter-regional power trading platform, and different users have different permissions to access the inter-provincial and inter-regional power trading platform, when using attribute-based encryption (ABE) to encrypt user data, the different effects of different types of data on user attributes are ignored, and a single key generation strategy is used, which makes the user data in the inter-provincial and inter-regional power trading platform have the risk of leakage when being accessed. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a user data protection method for an inter-provincial and inter-regional electricity trading platform to solve the existing problems.

[0005] The user data protection method for the inter-provincial and inter-regional power trading platform of this application adopts the following technical solutions: An embodiment of the present application provides a user data protection method for an inter-provincial and inter-regional power trading platform, the method comprising the following steps: Collect various power transaction data of each user in the power trading platform, including the user's transaction power; Based on the correlation between the transaction power of each user and other various power transaction data, a weight coefficient between the transaction power of each user and other various power transaction data is constructed; based on the differences between the same power transaction data of all users, combined with the weight coefficient, the transaction privacy sensitivity of various power transaction data other than the transaction power of each user is constructed; Based on the correlation between the same type of power transaction data of different users, the user transaction correlation of each type of power transaction data of each user is constructed; Determine the transaction attribute necessity of various power transaction data of each user other than the transaction power based on the transaction privacy sensitivity and the user transaction correlation; construct a necessary transaction attribute set of each user based on the transaction attribute necessity of the various power transaction data of each user; Based on the necessary transaction attribute set of each user, the encryption algorithm is used to encrypt and protect the data of each user.

[0006] In one embodiment, the process of obtaining the weight coefficient is as follows: The various electricity trading data of each user at the time of a preset number of transactions are used as the various historical trading data of each user; for each user, all kinds of historical electricity trading data of the user are used as the input of the partial correlation analysis algorithm, wherein the trading electricity is used as the dependent variable, and the other various electricity trading data are used as independent variables, and the output is the correlation coefficient between the user's trading electricity and the other various electricity trading data, which is recorded as the weight coefficient.

[0007] In one embodiment, the process of obtaining the transaction privacy sensitivity is as follows: The set of the same type of power transaction data of all users at the most recent transaction is recorded as a transaction attribute set, thereby obtaining each transaction attribute set of all users; For each transaction attribute set, all elements in the transaction attribute set are used as inputs of the clustering algorithm, and each output cluster is recorded as each first cluster of the transaction attribute set; Based on the number of elements in the first cluster and the weight coefficient, the transaction privacy sensitivity of various power transaction data of each user except the transaction amount is constructed.

[0008] In one embodiment, the transaction privacy sensitivity is expressed as: , where represents the transaction privacy sensitivity of the jth type of electricity transaction data of user i except the transaction amount, N represents the total number of users, represents the number of elements in the first cluster where the remaining j-th type of electricity transaction data of user i is located, Represents the weight coefficient between the transaction power of user i and the remaining j-th type of power transaction data.

[0009] In one embodiment, the process of obtaining the user transaction association is as follows: Obtain the transaction attribute sequence of each user; for each first cluster of each transaction attribute set, obtain the Euclidean distance between each element in the first cluster and the cluster center, and calculate the Kendall correlation coefficient between the transaction attribute sequence of each user in the first cluster and each other user; based on the Euclidean distance and the Kendall correlation coefficient, construct the user transaction correlation of each type of power transaction data of each user.

[0010] In one embodiment, the transaction attribute sequence is a sequence consisting of all types of electricity transaction data of each user at the time of the most recent transaction.

[0011] In one embodiment, the expression of the user transaction correlation is: , where Indicates the user transaction correlation of the jth type of power transaction data of user i except the transaction power; The Euclidean distance representing the remaining j-th type of electricity transaction data of user i; represents the kendall correlation coefficient between user i and the mth user in the first cluster where the remaining jth type of power transaction data of user i is located; Represents the number of elements in the first cluster where the remaining j-th type of electricity transaction data of user i is located; A positive number is preset artificially.

[0012] In one embodiment, the transaction attribute necessity is: the sum of the normalized transaction privacy sensitivity value of each type of electricity transaction data of each user except the transaction amount and the normalized value of the user transaction correlation.

[0013] In one embodiment, the process of obtaining the necessary transaction attribute set is as follows: The average transaction privacy sensitivity of all power transaction data of each user is calculated as the necessary segmentation threshold; the power transaction data of each user whose transaction privacy sensitivity is greater than or equal to the necessary segmentation threshold is taken as the necessary transaction attribute of each user, and the set of all necessary transaction attributes of each user is taken as the necessary transaction attribute set of each user.

[0014] In one embodiment, the process of encrypting and protecting each user's data is as follows: The necessary transaction attribute set of each user is used as the input of the attribute encryption algorithm, and the output is the first ciphertext and initial key of each user's data; the initial key and the preset encryption strategy are used as the input of the encryption algorithm based on the key strategy, and the output is the second ciphertext and the user's private key; when each user's data is transmitted, the data is encrypted by the first ciphertext and the second ciphertext of each user, and the data is decrypted by the user's private key.

[0015] This application has at least the following beneficial effects: The present application analyzes the relationship between transaction attributes and transaction behaviors and the differences between transaction attributes of different users through user data in an inter-provincial and inter-regional power trading platform, and constructs the transaction privacy sensitivity of various power trading data of each user. It takes into account the contribution of the user's transaction attributes to the transaction behavior and the differences between user needs and consumption habits, thereby improving the accuracy of the sensitivity analysis of various transaction attributes of users to the transaction behavior; based on the association relationship between the same power trading data of different users in the power trading platform, the user transaction correlation of each type of power trading data of each user is constructed, and the influence of different transaction attributes on the transaction behavior between users is analyzed; based on the transaction privacy sensitivity and user transaction correlation, the transaction attribute necessity of various power trading data of each user is constructed, the necessary transaction attributes of each user are obtained, and the user data is encrypted based on the necessary transaction attributes in combination with the AES algorithm and the CP-ABE algorithm, thereby reducing the risk of leakage of user data in the inter-provincial and inter-regional power trading platform when accessed due to the use of a single key generation strategy, and improving the security of user data access. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 A flow chart of a user data protection method for an inter-provincial and inter-regional power trading platform provided in this application; Figure 2 Schematic diagram of the process of obtaining user transaction correlation. DETAILED DESCRIPTION

[0018] 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 data protection method for the inter-provincial and inter-regional power trading 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.

[0019] 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.

[0020] The following is a detailed description of the specific scheme of the user data protection method for the cross-provincial and cross-regional electricity trading platform provided by this application in conjunction with the accompanying drawings.

[0021] An embodiment of the present application provides a method for protecting user data for an inter-provincial and inter-regional electricity trading platform.

[0022] Specifically, the following user data protection method for the inter-provincial and inter-regional power trading platform is provided, please refer to Figure 1 , the method comprises the following steps: Step S1, collecting various power transaction data of each user in the power transaction platform each time, including the user's transaction power.

[0023] Various power transaction data of each user at each transaction are obtained from the inter-provincial and inter-regional power transaction platform. The power transaction data of users include user type, transaction volume, transaction unit price, transaction time, maximum power load, minimum power load, power category, and contract type. Among them, user type refers to power generation enterprises, power sales companies, power users, etc., power category refers to industrial power, commercial power, residential power, etc., and contract type refers to long-term transaction contracts, temporary transaction contracts, etc.

[0024] All acquired user data is cleaned and incomplete and duplicate data is deleted.

[0025] Step S2, constructing a weight coefficient between each user's trading power and other various power trading data based on the correlation between each user's trading power and other various power trading data; based on the differences between the same power trading data of all users, combined with the weight coefficient, constructing the transaction privacy sensitivity of each user's various power trading data other than the trading power.

[0026] As an important hub of the regional power grid, the power trading platform conducts a large number of power trading activities all the time, and these trading activities involve many different user entities, and the content of the transactions also varies greatly. Different users have different access rights on the power trading platform. For example, a power sales company needs to trade with multiple power users. Power users can only view their own transaction records, while the power sales company can view the transaction records of all power users with whom it trades. Therefore, in order to ensure the security of user data, it is necessary to encrypt user data in the cross-provincial and cross-regional power trading platform. However, the traditional attribute-based encryption method adopts a single key generation strategy, ignoring the different effects of different types of data on user attributes, which makes the data leak risk when accessing.

[0027] (1) There are different trading behaviors on the power trading platform, such as temporary transactions, short-term transactions, long-term transactions, etc. Each transaction behavior contains corresponding transaction attributes, such as transaction amount, transaction type, role of the transaction subject, etc. There are significant differences between the transaction behaviors of different users, and the contribution of different users' transaction attributes to the transaction results is also quite different. For example, the transaction amount reflects the behavior patterns of large and medium-sized enterprises and small users; the transaction type, such as long-term transactions, temporary transactions, etc., directly affects the stability of power dispatch; the role of the transaction subject, such as power generation enterprises, power sales companies, power users, etc., determines the sensitivity of their data security.

[0028] Therefore, taking user i as an example, various power transaction data of user i during the most recent g transactions are obtained as various historical power transaction data of user i. Preferably, in the embodiment of the present application, the value of g is set to 100. As other embodiments of the present application, the implementer can set the value of g according to the actual situation. All kinds of historical power transaction data of user i are used as the input of the partial correlation analysis algorithm, wherein the transaction power is used as the dependent variable, and the remaining various power transaction data are used as independent variables. The correlation coefficient between the transaction power and the remaining various power transaction data is analyzed, and the output is the correlation coefficient between the transaction power of user i and the remaining various power transaction data, which is recorded as the weight coefficient. The larger the weight coefficient, the greater the contribution of the power transaction data corresponding to the weight coefficient to the transaction of user i. Partial correlation analysis is a well-known technology, and the specific process will not be repeated.

[0029] (2) Furthermore, there are significant differences in the sensitivity of different users to privacy protection in terms of their transaction attributes on the electricity trading platform. This difference mainly stems from the user's unique needs, consumption habits, and the user's role in the electricity market. For example, the electricity price strategy of a power sales company is directly related to its market competitiveness and commercial interests. Therefore, the unit price of such users in electricity transactions is often regarded as a trade secret, and the unit prices of electricity between different power sales companies often vary greatly. For ordinary electricity users, the difference between their electricity prices is small, so the sensitivity of electricity prices to privacy protection is relatively low. However, the differences between electricity trading frequency, electricity trading amount, and electricity load are large, which can reflect their living habits and therefore have greater privacy sensitivity. Therefore, the differences between various electricity trading data among different users are analyzed, and the transaction privacy sensitivity of each type of electricity trading data of each user is constructed, specifically: Obtain the power transaction data of each user in the power transaction platform at the time of the most recent transaction, and record the set consisting of the ath type of power transaction data of all users at the time of the most recent transaction as the ath transaction attribute set , thus obtaining each transaction attribute set of all users. For each transaction attribute set, all elements in the transaction attribute set are used as input of the AP (affinity propagation) clustering algorithm, and each output cluster is recorded as each first cluster of the transaction attribute set. The AP clustering algorithm is a well-known technology, and the specific process is not repeated here.

[0030] It should be noted that, for clustering all elements in the transaction attribute set, the implementer may also use other clustering algorithms to cluster all elements in the transaction attribute set, and this application does not make any specific restrictions.

[0031] The smaller the number of elements in the first cluster where the a-th type of electricity transaction data of user i is located, the more likely it is that the a-th type of electricity transaction data of user i has a higher privacy sensitivity for user i.

[0032] (3) Based on the above analysis, the transaction privacy sensitivity of user i’s various power transaction data other than the transaction volume is constructed, and the expression is: , where represents the transaction privacy sensitivity of the jth type of electricity transaction data of user i except the transaction amount, N represents the total number of users, represents the number of elements in the first cluster where the remaining j-th type of electricity transaction data of user i is located, Represents the weight coefficient between the transaction power of user i and the remaining j-th type of power transaction data.

[0033] The larger the weight coefficient of the remaining j-th type of electricity trading data of user i, the more important the attribute of the electricity trading data of user i is, and the more this attribute can affect the trading behavior of user i, and thus the greater the transaction privacy sensitivity is; the smaller the number of users in the cluster where the electricity trading data of user i is located, the greater the difference between user i and other users in the electricity trading data is, which means that the electricity trading data can more reflect the unique electricity trading characteristics of user i, and the higher the sensitivity of user i to the electricity trading data is, indicating that the electricity trading data is more likely to be a sensitive trading attribute of user i, and thus the transaction privacy sensitivity value is larger.

[0034] Step S3, constructing the user transaction correlation of each type of power transaction data of each user based on the correlation between the same type of power transaction data of different users; determining the necessity of transaction attributes of various power transaction data of each user except the transaction amount based on the transaction privacy sensitivity and the user transaction correlation; constructing the necessary transaction attribute set of each user based on the necessity of transaction attributes of the various other power transaction data of each user.

[0035] The transaction behaviors between different users in the power trading platform constitute a complex network, and there is a mutual dependence and support relationship between the various trading entities. However, due to the influence of factors such as the different needs of different users and changes in market strategies, different transaction attributes in the power trading platform affect the transaction behaviors between users to varying degrees.

[0036] The influence of different transaction attributes on the correlation between users is analyzed, and the user transaction correlation of each type of power transaction data of each user is constructed, specifically: (1) Record the sequence of all types of power transaction data of each user at the time of the most recent transaction as the transaction attribute sequence of each user; for each first cluster of each transaction attribute set, obtain the Euclidean distance between each element in the first cluster and the cluster center, and at the same time, calculate the Kendall correlation coefficient between the transaction attribute sequence of each user and each user in the first cluster. The calculation of the Euclidean distance and the Kendall correlation coefficient are both well-known technologies, and the specific process will not be repeated. The larger the Euclidean distance and the smaller the Kendall correlation coefficient, the weaker the association between each user and other users in the first cluster, and the more likely this type of power transaction data is a non-core transaction attribute of the user.

[0037] (2) Based on the above analysis, the user transaction correlation of each type of power transaction data other than the transaction volume of each user is calculated, and the expression is: , where Indicates the user transaction correlation of the jth type of power transaction data of user i except the transaction power; The Euclidean distance representing the remaining j-th type of electricity transaction data of user i; represents the kendall correlation coefficient between user i and the mth user in the first cluster where the remaining jth type of power transaction data of user i is located; Represents the number of elements in the first cluster where the remaining j-th type of electricity transaction data of user i is located; is a positive number that is artificially preset to prevent the denominator from being zero. The value of is set to 0.01. As other embodiments of the present application, the implementer can set it according to the actual situation. The value of .

[0038] Therefore, when the remaining j-th type of power trading data of user i in the power trading platform has a greater impact on the trading behavior between users, after clustering according to this type of power trading data in the power trading platform, the more similar user i is to other users in the first cluster in this type of power trading data, that is, the closer user i is to the center position in the first cluster, the smaller the Euclidean distance of this type of power trading data of user i; at the same time, the greater the impact of this type of power trading data on the trading behavior of user i, the more effective it is to cluster this type of power trading data to cluster users with similar trading behaviors, and the larger the calculated kendall correlation coefficient between the trading attributes of user i and other users in the cluster; and thus the greater the user transaction correlation of this attribute of user i, the greater the impact of this type of power trading data of user i on the trading behavior between users.

[0039] (3) For each user in the power trading platform, the transaction attribute necessity of each user's various power trading data other than the transaction power is calculated according to the transaction privacy sensitivity and the user transaction correlation, and the expression is: , where is the transaction attribute necessity of the jth type of power transaction data of user i except the transaction power, represents the transaction privacy sensitivity of the remaining j-th power transaction data of user i, represents the user transaction correlation degree of the remaining j-th type of power transaction data of user i, is the normalization function.

[0040] Calculate the average transaction privacy sensitivity of all the power transaction data of each user as the necessary segmentation threshold; take the power transaction data of each user whose transaction privacy sensitivity is greater than or equal to the necessary segmentation threshold as the necessary transaction attribute of each user, and take the set of all the necessary transaction attributes of each user as the necessary transaction attribute set of each user.

[0041] Step S4: Based on the necessary transaction attribute set of each user, each user's data is encrypted and protected in combination with an encryption algorithm.

[0042] The necessary transaction attribute set of each user in the power trading platform is used as the input of the AES algorithm, and the data of each user is encrypted, and the output is the first ciphertext and initial key of each user's data. The initial key and encryption strategy are used as the input of the CP-ABE (Ciphertext-Policy Attribute-Based Encryption) algorithm for encryption, and the output is the second ciphertext and the user's private key. Preferably, in the embodiment of the present application, the encryption strategy adopts the BSW scheme. As other embodiments of the present application, the implementer can set the encryption strategy according to the actual situation. Among them, the ASE algorithm, the CP-ABE algorithm and the BSW scheme are all well-known technologies, and the specific process will not be repeated. The first ciphertext and the second ciphertext of each user are used to encrypt the data of each user when transmitting it, and the data is decrypted by the user private key of each user, thereby realizing user data protection. Among them, the data encryption and decryption technology is a well-known technology, and the specific process will not be repeated.

[0043] It should be noted that, for the encryption of user data based on necessary transaction attributes, this application only provides one encryption method. There are many existing encryption methods. The implementer may also use other encryption algorithms for encryption, and this application does not make specific restrictions. For the acquisition of the second ciphertext, this application only provides one method for obtaining the ciphertext. There are many existing methods for obtaining the ciphertext. The implementer may also use other encryption algorithms to obtain the second ciphertext, and this application does not make specific restrictions.

[0044] The schematic diagram of the process of obtaining user transaction correlation is as follows: Figure 2 shown.

[0045] To summarize, the embodiment of the present application analyzes the relationship between transaction attributes and transaction behaviors and the differences between transaction attributes of different users through user data in a cross-provincial and cross-regional power trading platform, and constructs the transaction privacy sensitivity of various power trading data of each user. It takes into account the contribution of the user's transaction attributes to the transaction behavior and the differences between user needs and consumption habits, thereby improving the accuracy of the sensitivity analysis of various transaction attributes of the user to the transaction behavior; based on the association relationship between the same type of power trading data of different users in the power trading platform, the user transaction correlation of each type of power trading data of each user is constructed, and the influence of different transaction attributes on the transaction behavior between users is analyzed; based on the transaction privacy sensitivity and the user transaction correlation, the transaction attribute necessity of various power trading data of each user is constructed, the necessary transaction attributes of each user are obtained, and the user data is encrypted based on the necessary transaction attributes and in combination with the AES algorithm and the CP-ABE algorithm, thereby reducing the risk of leakage of user data in the cross-provincial and cross-regional power trading platform when accessed due to the use of a single key generation strategy, and improving the security of user data access.

[0046] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0047] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0048] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A user data protection method for an inter-provincial and inter-regional power trading platform, characterized in that: The method comprises the following steps: Collect various power transaction data of each user in the power trading platform, including the user's transaction power; Based on the correlation between the transaction power of each user and other various power transaction data, a weight coefficient between the transaction power of each user and other various power transaction data is constructed; based on the differences between the same power transaction data of all users, combined with the weight coefficient, the transaction privacy sensitivity of various power transaction data other than the transaction power of each user is constructed; Based on the correlation between the same type of power transaction data of different users, a user transaction correlation of each type of power transaction data of each user is constructed; based on the transaction privacy sensitivity and the user transaction correlation, the transaction attribute necessity of various power transaction data of each user other than the transaction amount is determined; based on the transaction attribute necessity of the various other power transaction data of each user, a necessary transaction attribute set of each user is constructed; Based on the necessary transaction attribute set of each user, the encryption algorithm is used to encrypt and protect the data of each user.

2. The user data protection method for an inter-provincial and inter-regional power trading platform according to claim 1, characterized in that: The process of obtaining the weight coefficient is as follows: The various electricity trading data of each user at the time of a preset number of transactions are used as the various historical trading data of each user; for each user, all kinds of historical electricity trading data of the user are used as the input of the partial correlation analysis algorithm, wherein the trading electricity is used as the dependent variable, and the other various electricity trading data are used as independent variables, and the output is the correlation coefficient between the user's trading electricity and the other various electricity trading data, which is recorded as the weight coefficient.

3. The user data protection method for an inter-provincial and inter-regional power trading platform according to claim 1, characterized in that: The process of obtaining the transaction privacy sensitivity is as follows: The set of the same type of power transaction data of all users at the most recent transaction is recorded as a transaction attribute set, thereby obtaining each transaction attribute set of all users; For each transaction attribute set, all elements in the transaction attribute set are used as inputs of the clustering algorithm, and each output cluster is recorded as each first cluster of the transaction attribute set; Based on the number of elements in the first cluster and the weight coefficient, the transaction privacy sensitivity of various power transaction data of each user except the transaction amount is constructed.

4. The user data protection method for an inter-provincial and inter-regional power trading platform according to claim 3, characterized in that: The expression of the transaction privacy sensitivity is: , where represents the transaction privacy sensitivity of the jth type of electricity transaction data of user i except the transaction amount, N represents the total number of users, represents the number of elements in the first cluster where the remaining j-th type of electricity transaction data of user i is located, Represents the weight coefficient between the transaction power of user i and the remaining j-th type of power transaction data.

5. The user data protection method for an inter-provincial and inter-regional power trading platform according to claim 3, characterized in that: The process of obtaining the user transaction correlation is as follows: Obtain the transaction attribute sequence of each user; for each first cluster of each transaction attribute set, obtain the Euclidean distance between each element in the first cluster and the cluster center, and calculate the Kendall correlation coefficient between the transaction attribute sequence of each user in the first cluster and each other user; based on the Euclidean distance and the Kendall correlation coefficient, construct the user transaction correlation of each type of power transaction data of each user.

6. The user data protection method for the inter-provincial and inter-regional power trading platform according to claim 5, characterized in that: The transaction attribute sequence is a sequence consisting of all types of power transaction data of each user at the time of the most recent transaction.

7. The user data protection method for an inter-provincial and inter-regional power trading platform according to claim 5, characterized in that: The expression of the user transaction correlation is: , where Indicates the user transaction correlation of the jth type of power transaction data of user i except the transaction power; The Euclidean distance representing the remaining j-th type of electricity transaction data of user i; represents the kendall correlation coefficient between user i and the mth user in the first cluster where the remaining jth type of power transaction data of user i is located; Represents the number of elements in the first cluster where the remaining j-th type of electricity transaction data of user i is located; A positive number is preset artificially.

8. The user data protection method for an inter-provincial and inter-regional power trading platform according to claim 1, characterized in that: The transaction attribute necessity is: the sum of the normalized transaction privacy sensitivity value of each type of electricity transaction data of each user except the transaction amount and the normalized value of the user transaction correlation.

9. The user data protection method for an inter-provincial and inter-regional power trading platform according to claim 1, characterized in that: The process of obtaining the necessary transaction attribute set is as follows: The average transaction privacy sensitivity of all power transaction data of each user is calculated as the necessary segmentation threshold; the power transaction data of each user whose transaction privacy sensitivity is greater than or equal to the necessary segmentation threshold is taken as the necessary transaction attribute of each user, and the set of all necessary transaction attributes of each user is taken as the necessary transaction attribute set of each user.

10. The user data protection method for an inter-provincial and inter-regional power trading platform according to claim 1, characterized in that: The process of encrypting and protecting each user's data is as follows: The necessary transaction attribute set of each user is used as the input of the attribute encryption algorithm, and the output is the first ciphertext and initial key of each user's data; the initial key and the preset encryption strategy are used as the input of the encryption algorithm based on the key strategy, and the output is the second ciphertext and the user's private key; when each user's data is transmitted, the data is encrypted by the first ciphertext and the second ciphertext of each user, and the data is decrypted by the user's private key.

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