A smart meter data aggregation method without a trusted institution

Abstract

The present invention discloses a method for aggregating data of smart meters without a trusted institution, which has the following characteristics and includes the following steps: Step 1, the aggregation center generates relevant parameters for each participant and sends them to the smart meter, and initializes the system. Step 2, the aggregation center creates a corresponding serial number for each smart meter based on a random sampling method and a quick sorting method. Step 3, each smart meter generates an n-dimensional vector according to its own serial number and sends it to the aggregation center, the aggregation center obtains all n-dimensional vectors and calculates the vector V agg Then, use the vector V agg Calculate any aggregation function. Step 4: Use the extended Shamir secret sharing method to manage the system.

Description

Technical Field

[0001] The present invention relates to the technical field of data aggregation, and in particular to a method for aggregating data of a smart meter without a trusted institution. Background Art

[0002] Smart grid is the next generation of power grid composed of existing power system and communication technology. Through command control and service query between control center and users, power companies can dynamically obtain overall power consumption, adjust electricity prices, provide additional power according to user needs or collect power from users. However, the security and privacy issues in this infrastructure are very challenging. Because users in smart grid cannot fully trust the cloud, and there may be malicious users who illegally obtain grid services or even damage the grid. Without proper protection, grid services can be easily damaged and user privacy can be easily leaked. Therefore, a reliable, efficient and secure privacy protection mechanism is essential for smart grid.

[0003] In order to protect the privacy of users, many studies have introduced data aggregation. Data aggregation is a common operation in IoT systems, and its basic purpose is to efficiently aggregate and collect data to improve energy consumption, network lifetime, traffic bottlenecks, and data accuracy. For example, the sum of the data in the aggregated area is published, and the published data has similar statistical characteristics to the original data, while the individual data is masked. Therefore, data aggregation is a feasible solution to balance utility and privacy protection.

[0004] In smart grids, smart meters are one of the most important components installed at the user end. Users report real-time electricity consumption data to the control center through smart meters, but this process brings some security and efficiency challenges: (1) User privacy may be leaked from real-time consumption data; (2) Smart meters usually have limited computing power, so it is impractical to perform some complex cryptographic operations. In addition, dynamic user management is also an important issue. User migration and changes in power companies will lead to changes in user membership. Due to the limited service life of smart meters or natural disasters, failures may often occur at the user end. This makes user management extremely complex and challenging. Summary of the invention

[0005] The present invention is made to solve the above-mentioned problem, and aims to provide a smart meter data aggregation method without a trusted institution.

[0006] The present invention provides a method for aggregating data of smart meters without a trusted institution, which has the following characteristics and includes the following steps: Step 1, the aggregation center generates relevant parameters for each participant and sends them to the smart meter, and initializes the system. Step 2, the aggregation center creates a corresponding serial number for each smart meter based on a random sampling method and a quick sorting method. Step 3, each smart meter generates an n-dimensional vector according to its own serial number and sends it to the aggregation center, the aggregation center obtains all n-dimensional vectors and calculates the vector V agg Then, use the vector V agg Calculate any aggregation function. Step 4: Manage the system using the extended Shamir secret sharing method.

[0007] In the smart meter data aggregation method without a trusted organization provided by the present invention, it can also have the following characteristics: wherein, step 1 specifically includes the following steps: step 1-1, the aggregation center generates relevant parameters for each participant and sends them to the smart meter. Step 1-2, after the smart meter receives the relevant parameters, it first uses the advanced encryption standard algorithm to generate two pairs of keys, and obtains their respective shared key sets through the aggregation center using the Diffie-Hellman algorithm. Secondly, the smart meter selects a random number, calculates the shares under different threshold conditions, encrypts their respective information to obtain ciphertext and uploads it to the aggregation center. Step 1-3, after the aggregation center receives the ciphertext, it shares it with other smart meters.

[0008] The method for aggregating smart meter data without a trusted institution provided by the present invention may also have the following feature: during step 1, the smart meter constructs a pseudo-random function.

[0009] In the smart meter data aggregation method without a trusted institution provided by the present invention, it can also have the following characteristics: wherein, step 2 specifically includes the following steps: step 2-1, in the specific serial number generation stage, the smart meter randomly selects sample values ​​of non-repeating integers using a random sampling method and uploads them to the aggregation center. Step 2-2, the aggregation center uses a quick sorting method to sort the sample values ​​from small to large to obtain a sorted set. Step 2-3, each smart meter obtains its own serial number according to the ranking of its own sample value in the set.

[0010] In the smart meter data aggregation method without a trusted organization provided by the present invention, it can also have the following characteristics: wherein, step 3 specifically includes the following steps: step 3-1, the aggregation center broadcasts the required threshold value and sends a list of online smart meters to each smart meter. Step 3-2, the online smart meters provide their respective partial shares to reconstruct the random value according to the Chinese remainder theorem. Step 3-3, each smart meter generates an n-dimensional vector according to the input data and its own serial number and sends it to the aggregation center. Step 3-4, after the aggregation center obtains all n-dimensional vectors, it performs corresponding addition operations on each item in the n-dimensional vector to calculate the vector V agg , using the vector V agg Computes arbitrary aggregate functions and computes arbitrary aggregate functions.

[0011] The method for aggregating smart meter data without a trusted institution provided by the present invention may also have the following features: wherein, in step 3-4, the vector V agg Contains information for all smart meters.

[0012] In the smart meter data aggregation method without a trusted institution provided by the present invention, it can also have the following characteristics: wherein, in step 4, when a new smart meter is added, steps 1 to 3 are repeated, and the aggregation center obtains the final aggregation result. When a smart meter is revoked from the system, the aggregation center sends a revocation message to the existing smart meter, after which the existing smart meter sends its encrypted share to the aggregation center, and the aggregation center broadcasts the required threshold according to the situation to reconstruct the random number, and then the existing smart meter continues to calculate the corresponding vector V i , the aggregation center will get the final aggregation result.

[0013] Functions and Effects of the Invention

[0014] According to the method for aggregating data of smart meters without a trusted institution involved in the present invention, the specific steps are as follows: Step 1, the aggregation center generates relevant parameters for each participant and sends them to the smart meter, and initializes the system. Step 2, the aggregation center creates a corresponding serial number for each smart meter based on a random sampling method and a quick sorting method. Step 3, each smart meter generates an n-dimensional vector according to its own serial number and sends it to the aggregation center, the aggregation center obtains all n-dimensional vectors and calculates the vector V agg Then, use the vector V agg Calculate any aggregation function. Step 4: Manage the system using the extended Shamir secret sharing method.

[0015] Therefore, the present invention aims at the problem of data privacy protection in the current smart grid, and proposes a smart meter data aggregation method without a trusted organization. Compared with other aggregation methods, it does not require a third-party trusted organization, and protects privacy by distributing specific serial numbers to users, which has better scalability in practical applications. Any aggregation operation of ciphertext can be realized according to requirements, and a threshold variable secret sharing algorithm based on the Chinese remainder theorem is used to support the management of dynamic users, resist the exit of malicious users, and has fault tolerance. The required security features are achieved, and the computing cost and communication overhead are effectively reduced.

[0016] In addition, the present invention can adopt various homomorphic encryption or symmetric encryption algorithms to prevent the leakage of sensitive user information and achieve (nk) source anonymity in order to solve the privacy leakage problem in the smart grid system. The sample values ​​are quickly sorted to generate specific sequence numbers, achieve low communication costs, and be implemented in smart meters with limited computing power. The threshold variable secret sharing algorithm based on the Chinese remainder theorem is used to allow users to dynamically join / exit the smart grid system and flexibly respond to different thresholds. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system model diagram of a smart meter data aggregation method without a trusted institution in an embodiment of the present invention;

[0018] Figure 2 It is a flow chart of a smart meter data aggregation method without a trusted institution in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the following embodiments and the accompanying drawings specifically illustrate a method for aggregating smart meter data without a trusted organization of the present invention.

[0020] Figure 1 This is a system model diagram of the smart meter data aggregation method without a trusted organization in this embodiment.

[0021] like Figure 1 As shown, the system of the smart meter data aggregation method without a trusted agency is divided into two types of entities: an untrusted aggregation center and smart meters.

[0022] 1. Smart meters: Smart meters collect real-time usage data i , generate a key, and upload the ciphertext to the aggregator. All smart meters in the system negotiate with each other to generate their shared key. Usually, smart meters are assumed to be untrustworthy. However, when reconstructing the key is needed, some smart meters with a certain trust relationship can cooperate. Smart meters can communicate with the aggregator through an insecure two-way communication channel.

[0023] 2. Aggregation Center: The aggregation center is mainly responsible for initializing system parameters, calculating and publishing the final results of the aggregation function. Generally, it is assumed that the aggregation center is honest but curious, it abides by the protocol and does not tamper with the calculation results. However, it may collude with malicious participants to infer some valuable information. We assume that there are at most n-2 participants who collude with the aggregator.

[0024] In this embodiment, a smart meter data aggregation method without a trusted institution is provided.

[0025] Figure 2 It is a flow chart of a smart meter data aggregation method without a trusted institution in an embodiment of the present invention.

[0026] like Figure 2 As shown, the smart meter data aggregation method without a trusted institution involved in this embodiment includes the following steps:

[0027] Step S1: The aggregation center generates relevant parameters for each participant and sends them to the smart meter, and initializes the system. The specific steps include:

[0028] Step S1-1, the aggregation center initializes the system by generating relevant parameters for each participant and sets flexible aggregation thresholds according to different situations, such as the failure probability of smart meters. The aggregation center selects n×m pairs of mutually prime integers by using a prime sequence generation algorithm. After completing the above steps, the aggregation center generates system parameters (q, G, g, n, t→t', P, a constant ) is sent to all smart meters.

[0029] Step S1-2, when the smart meter SM i When receiving the system parameters, two pairs of keys are first generated using the Advanced Encryption Standard algorithm and Smart meters exchange their public keys Each party obtains its own shared key set through the aggregation center using the Diffie-Hellman algorithm {S i,j} j∈[1,n],j≠i .

[0030] Secondly, the smart meter selects a deterministic function f and assumes that there are k (k≤n-2) smart meters that may collude with the aggregation center, and let M = {1, 2, ..., k}. Smart meter SM i Choose a random number Z i , and calculate z i =Z i +α×p0 and

[0031] Smart meters use prime number matrices to calculate z i and The shares under different thresholds, and then the smart meter encrypts its own information to obtain the ciphertext {e i,w} w∈[1,n] And upload it to the aggregation center.

[0032] Step S1-3, after the aggregation center receives all the ciphertexts, it will share them with other smart meters. i Using Z i and {S i,j} j∈[1,n],j≠i Construct pseudo-random function PF i,w (x) and PF i (x).

[0033] Step S2: The aggregation center creates a corresponding serial number for each smart meter based on a random sampling method and a quick sorting method. Specifically, the following steps are included:

[0034] Step S2-1, in the specific serial number generation stage, the smart meter SM i From the interval [1,n α+2 ] randomly select an integer sample value y i , where y i ≠y j for Upload to the aggregation center.

[0035] Step S2-2, when the aggregation center obtains all sample values ​​{y i} i∈[1,n] When , use the quick sort method to sort them from small to large and get the sorted set T.

[0036] Step S2-3, smart meter SM i According to the ranking of its sample value in the set T, it obtains its own unique sequence number Seq(i).

[0037] Step S3: Each smart meter generates an n-dimensional vector according to its own serial number and sends it to the aggregation center. The aggregation center obtains all n-dimensional vectors and calculates the vector V agg Then, use the vector V agg Calculate any aggregate function. It includes the following steps:

[0038] Step S3 - 1 : The aggregation center broadcasts the required threshold value and sends a list of online smart meters to each smart meter.

[0039] Step S3-2, the online smart meters provide their respective partial shares to reconstruct the random value z according to the Chinese Remainder Theorem i .

[0040] Step S3-3, each smart meter has an input data Di ∈[1,n] d , according to their specific serial numbers, the smart meter generates an n-dimensional vector And send it to the aggregation center.

[0041] Step S3-4, aggregate the centers to obtain all V i When it is i Each item in performs the corresponding addition operation to calculate V agg . Where V agg Contains information of all smart meters. Therefore, the aggregation center can use the vector V agg Computes arbitrary aggregate functions.

[0042] Step S4, manage the system using the extended Shamir secret sharing method. Specifically includes the following process:

[0043] Assume that a new smart meter (denoted as SM r ), which is similar to the steps in the initialization phase; first, the newly added smart meter generates two pairs of keys and sends the public key to the aggregation center; then the SM r Negotiate a shared key set and select a random number; since the original smart meter in the system r The additional shared keys are received, so the smart meters update their respective secret shared key sets; after that, the new set of smart meters shares the static secret to the aggregation center through the threshold variable secret sharing algorithm based on the Chinese remainder theorem, and continues to perform the same operations as the above part.

[0044] When a user is revoked from the system, it can no longer upload its key to the aggregation center; therefore, the aggregation center should send a revocation message to the existing smart meters; thereafter, the smart meters send their encrypted shares to the aggregation center; the aggregation center can broadcast the required threshold to reconstruct the random number according to the situation; then the smart meters continue to calculate the corresponding vector V i , the aggregation center will get the final aggregation.

[0045] Functions and Effects of the Embodiments

[0046] According to the smart meter data aggregation method without a trusted institution involved in this embodiment, the specific steps are as follows: Step 1, the aggregation center generates relevant parameters for each participant and sends them to the smart meter, and initializes the system. Step 2, the aggregation center creates a corresponding serial number for each smart meter based on a random sampling method and a quick sorting method. Step 3, each smart meter generates an n-dimensional vector according to its own serial number and sends it to the aggregation center. The aggregation center obtains all n-dimensional vectors and calculates the vector V agg Then, use the vector V aggCalculate any aggregation function. Step 4: Manage the system using the extended Shamir secret sharing method.

[0047] Therefore, the above embodiment is aimed at the problem of data privacy protection in the current smart grid, and proposes a smart meter data aggregation method without a trusted organization. Compared with other aggregation methods, it does not require a third-party trusted organization, and protects privacy by distributing specific serial numbers to users, which has better scalability in practical applications. Any aggregation operation of ciphertext can be implemented according to requirements. A threshold variable secret sharing algorithm based on the Chinese remainder theorem is used to support the management of dynamic users, resist the exit of malicious users, and is fault-tolerant. The required security features are achieved, and the computing cost and communication overhead are effectively reduced.

[0048] The above embodiments can adopt various homomorphic encryption or symmetric encryption algorithms to prevent the leakage of sensitive user information and achieve (nk) source anonymity in order to solve the privacy leakage problem in the smart grid system. The sample values ​​are quickly sorted to generate specific sequence numbers to achieve low communication costs and be implemented in smart meters with limited computing power. The threshold variable secret sharing algorithm based on the Chinese remainder theorem is used to allow users to dynamically join / exit the smart grid system and flexibly respond to different thresholds.

[0049] The above-mentioned embodiments are preferred examples of the present invention and are not intended to limit the protection scope of the present invention.

Claims

1. A method for aggregating smart meter data without a trusted institution, characterized in that: The following steps are involved: Step 1: The aggregation center generates relevant parameters for each participant and sends them to the smart meter, and initializes the system; Step 2, the aggregation center creates a corresponding serial number for each smart meter based on a random sampling method and a quick sorting method; Step 3: Each smart meter generates an n-dimensional vector according to its own serial number and sends it to the aggregation center. The aggregation center obtains all the n-dimensional vectors and calculates the vector V agg After that, use the vector V agg Compute arbitrary aggregate functions; Step 4, managing the system using an extended Shamir secret sharing method, Wherein, step 3 specifically includes the following steps: Step 3-1, the aggregation center broadcasts the required threshold value and sends a list of online smart meters to each smart meter; Step 3-2, the online smart meters provide their respective partial shares to reconstruct the random value according to the Chinese remainder theorem; Step 3-3, each of the smart meters generates an n-dimensional vector according to the input data and the respective serial numbers and sends it to the aggregation center; Step 3-4: After the aggregation center obtains all the n-dimensional vectors, a corresponding addition operation is performed on each item in the n-dimensional vector to calculate the vector V agg , using the vector V agg Computes arbitrary aggregate functions.

2. According to claim 1, a method for aggregating smart meter data without a trusted institution, Features: Wherein, step 1 specifically includes the following steps: Step 1-1, the aggregation center generates relevant parameters for each participant and sends them to the smart meter; Step 1-2, when the smart meter receives the relevant parameters, it first uses the Advanced Encryption Standard algorithm to generate two pairs of keys, and obtains their respective shared key sets through the aggregation center using the Diffie-Hellman algorithm. Secondly, the smart meter selects a random number, calculates the shares under different thresholds, encrypts their respective information to obtain ciphertext, and uploads it to the aggregation center; Step 1-3: After receiving the ciphertext, the aggregation center shares it with other smart meters.

3. The method for aggregating smart meter data without a trusted institution according to claim 1, characterized in that: in, During step 1, the smart meter constructs a pseudo-random function.

4. The method for aggregating smart meter data without a trusted institution according to claim 1, characterized in that: in, Step 2 specifically includes the following steps: Step 2-1, in the specific serial number generation stage, the smart meter randomly selects a sample value of a non-repeating integer using the random sampling method and uploads it to the aggregation center; Step 2-2, the aggregation center uses the quick sorting method to sort the sample values ​​from small to large to obtain a sorted set; Step 2-3: each of the smart meters obtains its own serial number according to the ranking of its own sample value in the set.

5. The method for aggregating smart meter data without a trusted institution according to claim 1, characterized in that: in, In step 3-4, the vector V agg Contains information about all of the smart meters.

6. The method for aggregating smart meter data without a trusted institution according to claim 1, characterized in that: in, In step 4, when a new smart meter is added, steps 1 to 3 are repeated, and the aggregation center obtains the final aggregation result; When the smart meter is revoked from the system, the aggregation center sends a revocation message to the existing smart meter, after which the existing smart meter sends its encrypted share to the aggregation center, which broadcasts the required threshold to reconstruct the random number according to the situation, and then the existing smart meter continues to calculate the corresponding vector V i , the aggregation center will get the final aggregation result.

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