Data aggregation method with fault tolerance and privacy protection functions

Abstract

The invention discloses a data aggregation method with fault tolerance and privacy protection functions, and relates to the technical field of smart power grid data security. The method comprises the following steps: setting security parameters, and issuing public parameters; enabling the intelligent electric meter to randomly select a first private key and a blind factor to calculate and publish a first public key, and enabling the aggregation gateway to randomly select a second private key to calculate and publish a second public key; encrypting the collected power consumption data of the user by using the intelligent electric meter to generate a data report; when the aggregation gateway receives the data report, executing a traditional data aggregation algorithm, and if the data report receives the data report, executing a data aggregation algorithm supporting fault tolerance to generate an aggregation ciphertext; and verifying the validity of the data and the time in the data center, and performing data recovery by using the private key. According to the method, data aggregation with fault-tolerant and privacy protection functions can be realized for two application conditions of a fault ammeter and a fault-free ammeter.

Description

Technical Field

[0001] This invention relates to the field of smart grid data security technology, and in particular to a data aggregation method with fault tolerance and privacy protection functions. Background Technology

[0002] In smart grids, smart meters in users' homes collect users' electricity consumption data. Data centers need to collect the sum of electricity consumption data from all users within their jurisdiction in a timely manner to perform data recovery operations and obtain aggregated data. However, current technology can only select new blind factors through a trusted third party after the blind factor used to calculate the aggregated data is leaked. This leads to high computational and storage costs for existing technologies. Therefore, current technologies face two urgent problems: First, how can data centers obtain the sum of electricity consumption data from all users within their jurisdiction without leaking the data of individual users? Second, how can data centers obtain the sum of electricity consumption data from other users within their jurisdiction when some smart meters malfunction? Summary of the Invention

[0003] The purpose of this invention is to provide a data aggregation method with fault tolerance and privacy protection functions, aiming to solve or improve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, the present invention provides the following solution: A data aggregation method with fault tolerance and privacy protection features includes: 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.

[0005] Optionally, the step of setting security parameters and publishing public parameters based on the security parameters specifically includes: 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 q * Indicates a number less than a prime number. The set of positive integers, ; Publish public parameters .

[0006] 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: 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. .

[0007] Optionally, 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.

[0008] Optionally, the step of using smart meters to encrypt the collected user electricity consumption data and 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 .

[0009] Optionally, the processing procedure of the traditional data aggregation algorithm 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 .

[0010] Optionally, 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.

[0011] Optionally, 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.

[0012] Optionally, when using the fault-tolerant 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, 2 represents the sum of user electricity consumption data when a faulty meter exists.

[0013] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention discloses a data aggregation method with fault tolerance and privacy protection functions. The method includes setting security parameters and publishing public parameters; having a smart meter randomly select a first private key and a blind factor to calculate and publish a first public key; having an aggregation gateway randomly select a second private key to calculate and publish a second public key; using the smart meter to encrypt the collected user electricity consumption data and generate a data report; and when the aggregation gateway receives the data report... If so, then the traditional data aggregation algorithm is executed. Then, a fault-tolerant data aggregation algorithm is executed to generate aggregated ciphertext; the validity of the data and time is verified in the data center, and data recovery is performed using the private key. This invention enables the data center to obtain the sum of electricity consumption data from other users even when some users' smart meters malfunction and fail to report their electricity consumption data. Even if the blind factor used by the smart meter at the current moment is leaked, the electricity consumption data of subsequently uploaded users will not be leaked. Furthermore, this invention does not require the use of a secure channel to transmit the blind factor. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram illustrating the steps of the data aggregation method with fault tolerance and privacy protection functions of the present invention; Figure 2 This is a schematic diagram of the aggregation process in this embodiment. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] The purpose of this invention is to provide a data aggregation method with fault tolerance and privacy protection functions, aiming to solve or improve at least one of the above-mentioned technical problems.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, this invention provides a data aggregation method with fault tolerance and privacy protection functions, including: Step 100: Set security parameters and publish public parameters based on the security parameters.

[0020] Step 200: 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.

[0021] Step 300: Use smart meters to encrypt the collected user electricity consumption data and generate a data report.

[0022] Step 400: 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.

[0023] Step 500: 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.

[0024] Based on the above technical solution, the following is provided: Figure 2 The embodiments and specific steps are shown.

[0025] Settings: Enter a security parameter The data center (DC) performs the following steps: 1) Select two large prime numbers , ,calculate , ;in Represents the least common multiple. Define a function. Random selection Make ,calculate ;in Let represent the greatest common divisor. Let the public key for Paillier homomorphic encryption be . The private key is .

[0026] 2) Choose a prime number of order. Multiplicative Elliptic Curves , yes One of the generators.

[0027] 3) Select two collision-stable hash functions , Among them, Z q * Indicates a number less than a prime number. The set of positive integers, .

[0028] 4) Publish public parameters .

[0029] Private key settings: Smart meter and aggregation gateway Perform the following steps.

[0030] 1) Smart Meters Randomly select the first private key and blinding factor Calculate the public key and and publicly released the first public key. .in, The parameter is the public key used to encrypt the signature portion (smart meter identity information). This is the public key used to encrypt the electricity consumption data.

[0031] 2) Aggregator Gateway Randomly select a second private key Calculate the second public key and publicly released the second public key. .

[0032] Blinding factor update: in time Any two smart meters and The specific steps for collaboratively updating one's own blinding factor are as follows: 1) calculate , And generate smart meters Updated blinding factor .

[0033] 2) calculate , And generate smart meters Updated blinding factor .

[0034] in, X i Indicates smart meter public key, X j Indicates smart meter public key, Indicates smart meter identity, Indicates smart meter His identity.

[0035] Notice: .therefore, .

[0036] Data Report: In Time , Perform the following steps.

[0037] 1) Random selection Calculate ciphertext ,in It is a smart meter Collected user electricity consumption data.

[0038] 2) Random selection Calculate at time T , , ;in, Indicates a value less than Integers that are greater than or equal to 1.

[0039] 3) Report the data Send to aggregation gateway .

[0040] When there are no faulty meters, perform traditional data aggregation: Aggregator Gateway receive Data Report Then, perform the following steps: 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.

[0041] 2) Calculation: .

[0042] 3) Random selection ,calculate , , .

[0043] 4) Aggregate ciphertext Send to data center .

[0044] Data recovery: Received from the aggregation gateway Report After that, the data center Perform the following steps: 1) Verify data and time T Validity: Calculation Verify the equation Is the equation true? If it is true, proceed with the following steps.

[0045] 2) Calculation , .

[0046] 3) Using the private key obtained through Paillier homomorphic encryption calculate ; 1 for all smart meters The sum of collected user electricity consumption data.

[0047] In addition, when faulty meters are present, fault-tolerant data aggregation is performed: 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: 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.

[0048] Data recovery: Received from the aggregation gateway Report After that, the data center Perform the following steps: 1) Verify data and time T Validity: Calculation Verify the equation Is the equation true? If it is true, proceed with the following steps.

[0049] 2) Calculation , .

[0050] 3) Using the private key obtained through Paillier homomorphic encryption calculate ; 2 refers to a smart meter that is functioning normally. The sum of collected user electricity consumption data.

[0051] In summary, in the process of determining the blind factor, smart meters Able to select blinding factors independently ,calculate And publicly announce the value Therefore, there is no need to transmit blind factors through secure channels. Before each report of user electricity consumption data, it will communicate with another smart meter. Collaborative updates to the blinding factor (the updated blinding factor and the unchanged blinding factor) The updated blind factor and the invariant ,in This involves indexing all smart meters participating in the blinding factor update, and then using the Paillier homomorphic encryption algorithm to blind and encrypt the data. Therefore, this application has the following beneficial effects: (1) When some users' smart meters malfunction and fail to report their electricity consumption data, the data center can still obtain the sum of the electricity consumption data of other users. (2) Before each data report is sent, the smart meter updates the blind factor. Even if the current time... KeT smart meter Blinding factor used It has been leaked, but it will not be leaked. Subsequent uploads of user electricity consumption data. (3) Smart meters Publicly announced values Without needing to transmit blinding factors through a secure channel. .

[0052] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0053] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

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.

2. The data aggregation method with fault tolerance and privacy protection functions according to claim 1, characterized in that, The setting of security parameters and the publication of public parameters based on these security parameters specifically includes: 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 q * Indicates a number less than a prime number. The set of positive integers, ; Publish public parameters .

3. The data aggregation method with fault tolerance and privacy protection functions according to claim 1, characterized in that, 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. .

4. 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.

5. 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 it to the aggregation gateway. .

6. 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 .

7. The data aggregation method with fault tolerance and privacy protection functions according to claim 6, 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.

8. 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.

9. The data aggregation method with fault tolerance and privacy protection functions according to claim 8, 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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