A data aggregation method with fault tolerance and privacy protection function

Abstract

The application discloses a data aggregation method with fault tolerance and privacy protection functions and relates to the technical field of smart grid data security. The method comprises the following steps: setting security parameters and publishing public parameters; letting smart meters randomly select a first private key and a blind factor to calculate and publish a first public key, and letting an aggregation gateway randomly select a second private key to calculate and publish a second public key; using the smart meters to encrypt user power consumption data collected by the smart meters to generate a data report; when the aggregation gateway receives the data report, if the data report is, a traditional data aggregation algorithm is executed, and if the data report is, a data aggregation algorithm supporting fault tolerance is executed to generate aggregation ciphertext; and the validity of data and time is verified in a data center, and data is recovered by using a private key. The application can realize data aggregation with fault tolerance and privacy protection functions for two application conditions of a faulty meter and a non-faulty meter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid data security, in particular to a data aggregation method with fault tolerance and privacy protection functions. BACKGROUND

[0002] In a smart grid, since the smart meter at home of a user can collect the user's power consumption data, the data center needs to collect the sum of the power consumption data of the users in the jurisdiction in time to perform data recovery operation to obtain aggregated data. The current technology can only select a new blind factor through a trusted third party again after the blind factor used for calculating the aggregated data is leaked, which leads to the problem of high calculation cost and storage cost of the prior art. Therefore, the current prior art has two problems to be solved: first, how can the data center obtain the sum of the power consumption data of all users in the jurisdiction without leaking the data of a single user; second, how can the data center obtain the sum of the power consumption data of other users in the jurisdiction when part of the smart meters fail. SUMMARY

[0003] The present application relates to the technical field of smart grid data security, in particular to a data aggregation method with fault tolerance and privacy protection functions.

[0004] To achieve the above object, the present application provides the following solutions.

[0005] A data aggregation method with fault tolerance and privacy protection functions comprises:

[0006] setting a security parameter and publishing a public parameter based on the security parameter;

[0007] making the smart meter randomly select a first private key and a blind factor to calculate a first public key, and making the aggregation gateway randomly select a second private key to calculate a second public key, and publishing the first public key and the second public key to the outside; wherein the blind factor is updated by any two smart meters cooperating;

[0008] encrypting the collected user power consumption data by the smart meter to generate a data report;

[0009] when the aggregation gateway receives data reports, if , a traditional data aggregation algorithm is executed, and if , a data aggregation algorithm supporting fault tolerance is executed to generate an aggregated ciphertext; wherein n represents the number of all smart meters generating data reports;

[0010] when the data center receives the aggregated ciphertext, the validity of the data and the time is verified, and the data is recovered by using the private key.

[0011] Optionally, the step of setting security parameters and publishing public parameters based on the security parameters specifically includes:

[0012] Set security parameters ;

[0013] Choose two large prime numbers and , ,calculate , Define function Random selection , making ,calculate Let the public key of Paillier homomorphic encryption be... The private key is ;in, Represents the least common multiple. Denotes the greatest common divisor. x Represent an integer, Indicates less than And with The set of coprime positive integers;

[0014] Choose a prime order Multiplicative Elliptic Curves And select the multiplicative elliptic curve group A generator ;

[0015] Choose two collision-stable hash functions , Among them, Z q * Indicates a number less than a prime number. The set of positive integers, ;

[0016] Publish public parameters .

[0017] Optionally, the step of having the smart meter randomly select a first private key and a blind factor to calculate a first public key, and having the aggregation gateway randomly select a second private key to calculate a second public key, and then publicly disclosing the first public key and the second public key, specifically involves:

[0018] Smart meters Randomly select the first private key and blinding factor Calculate the public key and public key and publicly released the first public key. ;

[0019] The aggregation gateway AG randomly selects a second private key , calculates a second public key , and publishes the second public key .

[0020] Optionally, the blind factor is updated by any two smart meters and at time , and the specific updating process is as follows:

[0021] The smart meter calculates the parameters and respectively, and generates the updated blind factor of the smart meter ;

[0022] The smart meter calculates the parameters and respectively, and generates the updated blind factor of the smart meter ;

[0023] wherein, X i represents the public key of the smart meter , X j represents the public key of the smart meter , represents the identity of the smart meter , represents the identity of the smart meter .

[0024] Optionally, the collected user power consumption data is encrypted by the smart meter, and a data report is generated, specifically as follows:

[0025] The smart meter randomly selects a parameter , and calculates a ciphertext ; wherein, represents a set of positive integers less than and coprime with , represents the user power consumption data collected by the smart meter , represents the updated blind factor of the smart meter ;

[0026] A parameter is randomly selected, and at time , , ;in, Indicates a value less than Integers that are greater than or equal to 1;

[0027] Generate data report And send to the aggregation gateway .

[0028] Optionally, the processing procedure of the traditional data aggregation algorithm is as follows:

[0029] When the aggregation gateway receive Data Report Afterwards, verification report and time. T Validity: Calculation Verify the equation If the equation holds true, then calculate the ciphertext:

[0030] ;

[0031] Random selection ,calculate , , ;

[0032] Aggregate ciphertext Send to data center .

[0033] Optionally, when using the traditional data aggregation algorithm, the process of processing the aggregated ciphertext in the data center (DC) is as follows:

[0034] First, verify the data and time. T Validity: Calculation Verify the equation Does the equation hold true? If it does, then calculate:

[0035] ;

[0036] ;

[0037] Then, using the private key obtained through Paillier homomorphic encryption. calculate ;in, This is the sum of user electricity consumption data when there are no faulty meters.

[0038] Optionally, the processing procedure of the fault-tolerant data aggregation algorithm is as follows:

[0039] Using aggregation gateway The set of indexes of the normally functioning smart meters corresponding to the received data reports is denoted as . , the broadcasting is given to all smart meters and data center , and the smart meter re-performs the blind factor updating and data report generating steps;

[0040] The aggregated gateway calculates the ciphertext ; wherein, represents the user electricity data collected by the smart meter , represents the updated blind factor, represents the number randomly selected from .

[0041] Optionally, when the data aggregation algorithm supporting fault tolerance is adopted, the process of processing the aggregated ciphertext in the data center DC is:

[0042] First, verify the validity of data and time T : calculate , verify whether the equation is established, and if the equation is established, calculate:

[0043] ;

[0044] ;

[0045] Then, the private key of the Paillier homomorphic encryption is used to calculate ; wherein, 2 is the sum of the user electricity data when there is a faulty meter.

[0046] According to the specific embodiments provided by the application, the following technical effects are disclosed:

[0047] The application discloses a data aggregation method with fault tolerance and privacy protection function, which comprises the following steps: setting security parameters and publishing public parameters; the smart meter randomly selects a first private key and a blind factor to calculate and publish a first public key, and the aggregated gateway randomly selects a second private key to calculate and publish a second public key; the user electricity data collected by the smart meter is encrypted to generate a data report; when the aggregated gateway receives the data report , the traditional data aggregation algorithm is executed, and if it is ​If the condition is met, a data aggregation algorithm supporting fault tolerance is executed to generate aggregation ciphertext; the validity of data and time is verified in the data center, and data recovery is performed by using a private key. The application can obtain the sum of power consumption data of other users even if the smart meter of part of users fails to report power consumption data, and even if the blind factor used by the smart meter at the current time is leaked, the power consumption data of subsequent uploaded users will not be leaked, and the application does not need to use a secure channel to transmit the blind factor. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only represent some of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 A schematic diagram of the steps of the data aggregation method with fault tolerance and privacy protection function of the present application;

[0050] Figure 2 A schematic diagram of the aggregation process in the present embodiment. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0052] The present application aims to provide a data aggregation method with fault tolerance and privacy protection function, aiming to solve or improve at least one of the above technical problems.

[0053] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0054] As shown in the drawings, the present application provides a data aggregation method with fault tolerance and privacy protection function, comprising: Figure 1 Step 100: setting security parameters and issuing public parameters based on the security parameters.

[0055]

[0056] ​Step 200: Let the smart meter randomly select a first private key and a blind factor to calculate a first public key, and let the aggregation gateway randomly select a second private key to calculate a second public key, and publish the first public key and the second public key; wherein the blind factor is updated by any two smart meters cooperating.

[0057] Step 300: Use the smart meter to encrypt the collected user power consumption data to generate a data report.

[0058] Step 400: When the aggregation gateway receives data reports, if , a traditional data aggregation algorithm is executed, and if , a fault-tolerant data aggregation algorithm is executed to generate an aggregated ciphertext; wherein n represents the number of all smart meters generating data reports.

[0059] Step 500: When the data center receives the aggregated ciphertext, the validity of the data and the time is verified, and the data is recovered using the private key.

[0060] Based on the above technical solution, an embodiment and specific steps are provided as shown in Figure 2 .

[0061] Setting: input a security parameter , the data center (DC) executes the following steps:

[0062] 1) Select two large prime numbers , , calculate , ; wherein represents the least common multiple. Define a function , randomly select such that , calculate ; wherein represents the greatest common divisor. Let the public key of the Paillier homomorphic encryption be , and the private key be .

[0063] 2) Select a multiplicative elliptic curve group of prime order , is a generator of .

[0064] 3) Select two collision-resistant hash functions , ; wherein, Z q * represents the set of positive integers less than the prime number , .

[0065] 4) Publish public parameters .

[0066] Private key setup: smart meter and aggregation gateway The following steps are performed.

[0067] 1) Smart meter Randomly select a first private key and a blind factor , calculate a public key and , and publish the first public key outside. Among them, is the encryption public key for the signature part (smart meter identity information), and the parameter is the encryption public key for the power consumption data part.

[0068] 2) Aggregation gateway Randomly select a second private key , calculate a second public key , and publish the second public key outside.

[0069] Blind factor update: At time , any two smart meters and cooperate to update their blind factors, and the specific steps are as follows:

[0070] 1) Calculate , , and generate the updated blind factor of the smart meter .

[0071] 2) Calculate , , and generate the updated blind factor of the smart meter .

[0072] Among them, X i represents the public key of the smart meter , X j represents the public key of the smart meter , represents the identity of the smart meter , represents the identity of the smart meter .

[0073] Notice: .therefore, .

[0074] Data Report: In Time , Perform the following steps.

[0075] 1) Random selection Calculate ciphertext ,in It is a smart meter Collected user electricity consumption data.

[0076] 2) Random selection Calculate at time T , , ;in, Indicates a value less than Integers that are greater than or equal to 1.

[0077] 3) Report the data Send to aggregation gateway .

[0078] When there are no faulty meters, perform traditional data aggregation:

[0079] Aggregator Gateway receive Data Report Then, perform the following steps:

[0080] 1) Verify the validity of the report and time T: Calculate Verify the equation Is the equation true? If it is true, proceed with the following steps.

[0081] 2) Calculation: .

[0082] 3) Random selection ,calculate , , .

[0083] 4) Aggregate ciphertext Send to data center .

[0084] Data recovery: Received from the aggregation gateway Report After that, the data center Perform the following steps:

[0085] 1) Verify data and time T Validity: Calculation Verify the equation Is the equation true? If it is true, proceed with the following steps.

[0086] 2) Calculation , .

[0087] 3) Using the private key obtained through Paillier homomorphic encryption calculate ; 1 for all smart meters The sum of collected user electricity consumption data.

[0088] In addition, when faulty meters are present, fault-tolerant data aggregation is performed:

[0089] 1) If the aggregation gateway Only received Data Report Aggregator Gateway The smart meter corresponding to the received report (working normally / not malfunctioning) The index set is denoted as ,Will Broadcast to all and data centers And let these meters Re-execute the "blind factor update" and "data reporting" algorithms. The following results will be obtained:

[0090] 2) Aggregator Gateway Compute aggregate ciphertext ;in, Indicates smart meter Collected user electricity consumption data, This represents the updated blinding factor. Indicates from A number randomly selected from the list.

[0091] Data recovery: Received from the aggregation gateway Report After that, the data center Perform the following steps:

[0092] 1) Verify data and time T Validity: Calculation Verify the equation Is the equation true? If it is true, proceed with the following steps.

[0093] 2) Calculation , .

[0094] 3) Using the private key obtained through Paillier homomorphic encryption computing ; 2 is the normal working smart meter the sum of the user's electricity data collected.

[0095] In summary, in the process of determining the blind factor, the smart meter can select the blind factor by itself, calculate and publish the value to the outside, so that the blind factor does not need to be transmitted through a secure channel Before reporting the user's electricity data each time, it cooperates with another smart meter to update the blind factor (their updated blind factor and the unchanged , and then the updated blind factor and the unchanged , wherein represents the index of all smart meters participating in the blind factor update), and then uses the Paillier homomorphic encryption algorithm to blind and encrypt the data. It can be seen that the present application has the following beneficial effects:

[0096] (1) When some of the user's smart meters fail to report the electricity data, the data center can still obtain the sum of the electricity data of other users. (2) Before sending the data report each time, the smart meter will update the blind factor. Even if the blind factor used by the smart meter at the current time leaks, the user's electricity data uploaded subsequently will not be leaked. (3) The smart meter publishes the value to the outside, and does not need to transmit the blind factor through a secure channel. .

[0097] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be mutually referred to.

[0098] The principles and implementation modes of the present application are described by applying specific examples in this paper. The above description of the embodiments is only used to help understand the core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A data aggregation method with fault tolerance and privacy protection functions, characterized in that, include: Set security parameters, and publish public parameters based on the security parameters; The smart meter randomly selects a first private key and a blind factor to calculate a first public key, and the aggregation gateway randomly selects a second private key to calculate a second public key, and publishes the first public key and the second public key to the outside world; wherein, the blind factor is updated by any two smart meters in cooperation; Smart meters are used to encrypt collected user electricity consumption data and generate data reports. When the aggregation gateway receives When reporting individual data, if If so, then the traditional data aggregation algorithm is executed. Then, a fault-tolerant data aggregation algorithm is executed to generate aggregated ciphertext; where, n This indicates the number of data reports generated by all smart meters; When the data center receives the aggregated ciphertext, it verifies the validity of the data and time, and uses the private key to recover the data. The setting of security parameters and the publication of public parameters based on these security parameters specifically involves: Set security parameters ; Choose two large prime numbers and , ,calculate , Define function Random selection , making ,calculate Let the public key of Paillier homomorphic encryption be... The private key is ;in, Represents the least common multiple. Denotes the greatest common divisor. x Represent an integer, Indicates less than And with The set of coprime positive integers; Choose a prime order Multiplicative Elliptic Curves And select the multiplicative elliptic curve group A generator ; Choose two collision-stable hash functions , Among them, Z Indicates a number less than a prime number. The set of positive integers, ; Publish public parameters ; The process involves having the smart meter randomly select a first private key and a blind factor to calculate a first public key, and having the aggregation gateway randomly select a second private key to calculate a second public key, and then publicly disclosing the first and second public keys. Specifically: Smart meters Randomly select the first private key and blinding factor Calculate the public key and public key and publicly released the first public key. ; Instruct the aggregation gateway AG to randomly select a second private key. Calculate the second public key and publicly released the second public key. .

2. The data aggregation method with fault tolerance and privacy protection functions according to claim 1, characterized in that, The blind factor is composed of any two smart meters. and In time Collaborative updates, the specific update process is as follows: Using smart meters Calculate the parameters separately and parameters And generate smart meters Updated blinding factor ; Using smart meters Calculate the parameters separately and parameters And generate smart meters Updated blinding factor ; in, X i Indicates smart meter public key, X j Indicates smart meter public key, Indicates smart meter identity, Indicates smart meter His identity.

3. The data aggregation method with fault tolerance and privacy protection functions according to claim 1, characterized in that, The process of encrypting collected user electricity consumption data using smart meters to generate a data report specifically involves: Smart meters Randomly select parameters Calculate ciphertext ;in, Indicates less than And with The set of coprime positive integers. Indicates smart meter Collected user electricity consumption data, Indicates smart meter Updated blinding factor; Randomly select parameters Calculate in time time , , ;in, Indicates a value less than Integers that are greater than or equal to 1; Generate data report And send to the aggregation gateway .

4. The data aggregation method with fault tolerance and privacy protection functions according to claim 1, characterized in that, The traditional data aggregation algorithm's processing procedure is as follows: When the aggregation gateway receive Data Report Afterwards, verification report and time. T Validity: Calculation Verify the equation If the equation holds true, then calculate the ciphertext: ； Random selection ,calculate , , ; Aggregate ciphertext Send to data center .

5. The data aggregation method with fault tolerance and privacy protection functions according to claim 4, characterized in that, When using the traditional data aggregation algorithm, the process of processing the aggregated ciphertext in the data center (DC) is as follows: First, verify the data and time. T Validity: Calculation Verify the equation Does the equation hold true? If it does, then calculate: ； ； Then, using the private key obtained through Paillier homomorphic encryption. calculate ;in, This is the sum of user electricity consumption data when there are no faulty meters.

6. The data aggregation method with fault tolerance and privacy protection functions according to claim 1, characterized in that, The processing procedure of the fault-tolerant data aggregation algorithm is as follows: Using aggregation gateway The set of indexes of the normally functioning smart meters corresponding to the received data reports is denoted as . ,Will Broadcast to all smart meters and data centers And make smart meters Repeat the blind factor update and data report generation steps; Using aggregation gateway Calculate ciphertext ;in, Indicates smart meter Collected user electricity consumption data, This represents the updated blinding factor. Indicates from A number randomly selected from the list.

7. The data aggregation method with fault tolerance and privacy protection functions according to claim 6, characterized in that, When the fault-tolerant data aggregation algorithm is used, the process of processing the aggregated ciphertext in the data center (DC) is as follows: First, verify the data and time. T Validity: Calculation Verify the equation Does the equation hold true? If it does, then calculate: ； ； Then, using the private key obtained through Paillier homomorphic encryption. calculate ;in, 2 represents the sum of user electricity consumption data when a faulty meter exists.

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