A smart grid multi-dimensional data adaptive efficient aggregation method, system, device and medium supporting privacy protection

Abstract

A kind of smart grid multi-dimensional data adaptive efficient aggregation method, system, device and medium supporting privacy protection, step 1, control center, gateway and smart meter initialization: generating bilinear pairing parameters (q, P, G1, G2, e), generating paillier public key cryptosystem parameters public key pk and private key sk and generating the aggregation parameters required by Chinese remainder theorem;Step 2, report generation: user-side smart meter generates report and transmits to gateway;Step 3, gateway receives user-side report and carries out data aggregation and batch verification, generates aggregation report, and transmits aggregation report to control center;Step 4, control center receives aggregation report and carries out data decryption;System, device and medium are used to realize the present application;The present application converts multi-dimensional power consumption data into multi-dimensional shared secret by using shamir multi-secret sharing method, omits using data aggregation algorithm on multi-dimensional power consumption data, and further reduces the number of encryption, finally reduces the computing overhead of user side.

Description

Technical Field

[0001] This invention belongs to the field of information security technology, specifically relating to a method, system, device, and medium for adaptive and efficient aggregation of multi-dimensional data in smart grids that supports privacy protection. Background Technology

[0002] The smart grid is a next-generation power system that integrates modern advanced information and communication technologies. Compared to the traditional power grid, it can integrate and share multiple energy sources, improve energy utilization efficiency and system responsiveness, ensure the safe and stable operation of the power system, realize intelligent management and application of the power grid, and improve the safety, reliability, economy, and sustainable development capabilities of the power system.

[0003] In a smart grid, the Control Center (CC) periodically collects consumer electricity data from smart meters (SMs) installed in various locations to formulate optimal power distribution strategies. However, user electricity consumption data is highly sensitive for users. Criminals can use the amount of electricity consumed at different times to reveal users' lifestyles and even their financial situations, potentially leading to criminal activity. Therefore, if electricity consumption data is directly uploaded, criminals could obtain users' true electricity usage data through eavesdropping or other means, which is extremely dangerous for users. Similarly, users do not want the GW or CC to access their electricity consumption data, as there is a possibility of collusion between the Gateway (GW) or CC and criminals. Furthermore, considering that in a smart grid environment, users are no longer just consumers but often also producers (i.e., "prosumers"), meaning they may generate electricity using distributed power sources (such as solar photovoltaic panels, micro gas turbines, etc.). In this context, the data recorded by smart meters includes not only users' electricity consumption data (such as specific electricity usage time, electricity consumption, and electrical equipment), but may also include electricity generation data (such as specific electricity generation time, electricity generation, and electrical equipment). In other words, the data recorded by smart meters is typically multi-dimensional.

[0004] Existing solutions: Reference 1 (Zhao, S., Xu, S., Han, S., Ren, S., Wang, Y., Chen, Z., ... & Liu, W. (2023). PPMM-DA: Privacy-preserving multi-dimensional and multi-subset data aggregation with differential privacy for fog-based smart grids. IEEE Internet of Things Journal.) Utilizing the Chinese Remainder Theorem to achieve multidimensional and multi-subset data aggregation resistant to various attacks; Reference 2 (Chen, D., Zhou, T., Liu, W., Li, R., Wu, L., & Yang, X. (2023). MDA-FLH: Multidimensional Data Aggregation Scheme with Fine-grained Linear Homomorphism for Smart Grid. IEEE Internet of Things Journal Similarly, the Chinese Remainder Theorem is used to implement a multidimensional data aggregation algorithm that can provide linear homomorphic operations for each dimension.

[0005] However, the aforementioned solutions all use methods such as the Chinese Remainder Theorem to transform multidimensional data into one-dimensional data before encryption and transmission, thereby reducing the number of encryption steps. To further reduce computational overhead, it's necessary to aggregate the time dimension data, but this secondary aggregation leads to an exponential increase in the required data size, significantly increasing computational and storage costs on the user's end. Since smart meters on the user's end have limited computing resources, these methods impose a heavy computational burden on smart meters. Therefore, these solutions still suffer from the problem of high user-end overhead. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention aims to provide a method, system, device and medium for adaptive and efficient aggregation of multi-dimensional data in smart grids that supports privacy protection. By utilizing the Shamir multi-secret sharing method, multi-dimensional electricity consumption data is transformed into multi-dimensional shared secrets, eliminating the need for data aggregation algorithms on multi-dimensional electricity consumption data, thereby reducing the number of encryption operations and ultimately reducing the computational overhead at the user end.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A privacy-preserving adaptive and efficient aggregation method for multidimensional data in smart grids includes the following steps: Step 1, Initialize the control center, gateway, and smart meters: Generate bilinear pairing parameters Generate the public key pk and private key sk parameters of the Paillier public-key cryptosystem, as well as the aggregation parameters required for generating the Chinese Remainder Theorem; Step 2, Report Generation: After the smart meter at the user end generates a report, it is transmitted to the gateway; Step 3: After receiving the user's report, the gateway performs data aggregation and batch verification, generates an aggregation report, and transmits the aggregation report to the control center. Step 4: After receiving the aggregation report, the control center decrypts the data.

[0008] The specific method for step 1 is as follows: Control Center (CC) selects a large prime number. According to safety parameters Calling the bilinear parameter generation algorithm Generate bilinear pairing parameters Choose a safe hash function ; Control Center (CC) selects two prime numbers and ,satisfy ,calculate as well as Define function Select generator And calculate Then the public key is The private key is ; Control Center CC Selection For coprime prime numbers ,in It is the maximum number of delays. , ,in It is the upper limit of the first dimension of data. This is the maximum number of users that the gateway can connect to, calculated by the control center (CC). ,satisfy ,as well as and Control Center CC announced ; Smart meters Choose one Use it as your private key and calculate Each gateway selects one Use it as your private key and calculate Smart meters and gateways publish their respective calculated pairing parameters. and .

[0009] The specific method for step 2 is as follows: user smart meters Electricity consumption data is collected at time t. Then generate Polynomial of degree: ,in It's a random number, from a smart meter. according to generate A set of coordinate points ,in Smart meters Will Local collections exist Smart meters Then use your own private key for Calculate signature , Will Passed to the gateway .

[0010] The specific method for step 3 is as follows: gateway First check user ID and time To check if it is correct, then verify if the following formula holds true:

[0011] If the above formula is true, then the signature is valid, and its correctness is as follows:

[0012] After verifying that the equation holds true, the gateway will sum up all the collected coordinate points to obtain: } in, The gateway uses its own private key. for Calculate signature ,Will Transmitted to the control center (CC).

[0013] The specific method for step 4 is as follows: After receiving the aggregated data results from the gateway, the control center (CC) first checks the gateway. and time Check if it is correct, and then verify the validity of the signature according to the following formula. If the equation is true, the signature is valid, and the data is stored locally.

[0014] When the control center (CC) needs to obtain aggregated data results or the delay count reaches... At that time, a query request is sent to the gateway (GW), which then forwards the query request to the smart meter at the user's end. Smart meters Multiple sets of coordinates stored locally at different times Aggregation using CRT:

[0015] Next, smart meters plaintext encryption:

[0016] in, , Smart meters Calculate signature Smart meters Will Passed to the gateway After confirming that the control center (CC) has successfully received the data, clear the coordinate set. ; gateway First check user ID and time To verify if it is correct, then check if the signature is valid using the following formula:

[0017] After verifying that the equation is true and the signature is valid, multiply all the collected ciphertext together to obtain:

[0018] The gateway uses its own private key for Calculate signature ,Will Transmitted to the control center (CC); After receiving the aggregation results from the gateway, the Control Center (CC) first checks the gateway. and time To verify if it is correct, then check if the signature is valid using the following formula:

[0019] Once the equation is verified and the signature is valid, the data is sent to the smart meter. Inform the user that the data was successfully received, and then decrypt using the following formula. :

[0020] in According to the Chinese Remainder Theorem, the control center CC is obtained through the following formula. :

[0021] The control center (CC) based on the data already received locally. The sum of multidimensional electricity consumption data at the corresponding time point is obtained using the Lagrange interpolation method of the following formula:

[0022] Except for the last coefficient, the coefficient of each item is a sum of electricity consumption data in one dimension; when Without performing a delay operation, this algorithm can be transformed from a delayed algorithm to a real-time algorithm, i.e., a smart meter. Each time, a set of coordinate points is obtained. After encrypting the first piece of data, you can upload all the data directly.

[0023] This invention also provides a privacy-preserving smart grid multi-dimensional data adaptive high-efficiency aggregation system, comprising: System initialization module: Used to initialize the control center, gateway, and smart meters, and generate bilinear pairing parameters. Generate the public key pk and private key sk parameters of the Paillier public-key cryptosystem, as well as the aggregation parameters required for generating the Chinese Remainder Theorem; Smart meter report generation module: used to generate reports from user-end smart meters and transmit them to the gateway; Aggregated report generation module: This module is used to aggregate and batch verify data after the gateway receives reports from the user end, generate an aggregated report, and transmit the aggregated report to the control center. Data decryption module: Used to decrypt data after the control center receives the aggregated report.

[0024] This invention also provides a privacy-protected smart grid multi-dimensional data adaptive high-efficiency aggregation device, comprising: Memory: A computer program that stores the above-mentioned method for adaptive and efficient aggregation of multidimensional data in a smart grid that supports privacy protection, and is a computer-readable device; Processor: Used to implement the privacy-preserving smart grid multidimensional data adaptive and efficient aggregation method when executing the computer program.

[0025] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, enables the implementation of the aforementioned privacy-preserving smart grid multidimensional data adaptive and efficient aggregation method.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In step 2 of this invention, by utilizing Shamir's multi-secret sharing to process electricity data, it is possible to efficiently package and process multidimensional data from multiple moments with only one aggregation operation and one encryption operation. It supports adaptive adjustment of delay parameters, that is, it can adjust how many moments of data are packaged at once. It supports the control center CC to obtain the aggregation results at any time, and even supports the control center CC to obtain the aggregation results in real time.

[0027] 2. This invention aggregates electricity consumption data over a specified time dimension, enabling data aggregation within a specified time length with only one aggregation operation required, thus reducing the computational overhead of smart meters on the user end.

[0028] In summary, this invention efficiently packages and processes multidimensional data from multiple time points with only one aggregation operation and one encryption operation. Furthermore, the control center (CC) can obtain the aggregation results at each time point. This invention supports adaptive adjustment of the delay parameter, allowing adjustment of how many time points of data to be packaged at once. It also supports the control center CC obtaining the aggregation results at any time, even in real-time. This invention also demonstrates that delaying the acquisition of aggregation results significantly outperforms other homomorphic encryption methods as the delay parameter increases. Even with real-time acquisition of aggregation results, the performance of this method is comparable to or even better than other homomorphic encryption methods. Attached Figure Description

[0029] Figure 1 This is a simplified system structure diagram of the present invention.

[0030] Figure 2 This is a flowchart illustrating the completion of one cycle of the present invention. Detailed Implementation

[0031] This invention is attached Figure 1 Taking a simple system structure as an example, this invention illustrates how two smart meters (SMs) perform multi-dimensional data aggregation; the process is similar for multiple smart meters (SMs). This invention uses the BLS short signature algorithm for data integrity verification. While this algorithm has mature applications, its specific calculations are quite complex. Therefore, in this embodiment, the data integrity verification step is omitted. Furthermore, this invention calls the Python PHEY library to implement the initialization and encryption / decryption calculations of the Paillier public-key cryptosystem. The main flow of the embodiment is as follows: Figure 2 As shown.

[0032] like Figure 1 As shown, step 1 initializes the control center, gateway, and smart meters: generating the aggregation parameters required for the Paillier public-key cryptosystem and the Chinese Remainder Theorem; Specifically, first, generate the parameters required for the Paillier public-key cryptosystem. Assuming the security parameter is 1024, using the Paillier generation function, we have: p = 11233003211553317591007169132824406907813366891711216127625207616301397128443712014314397646711603170593697113773312650802925718 394002022578278766289576853,q=13125702318017038213535884199735341274348004991036203558890975047938508961919231774566550465676375948983154354073718584347145651039785773850868888957280173. Assume the delay parameter k=1, meaning that CC obtains electricity consumption statistics after two time points. Then, the parameters required for the Chinese Remainder Theorem are generated, choosing p1=53, p2=59, P=p1p2=3127, P1=59, P2=52, y1=9, y2=49. This step generates the parameters needed for subsequent encryption, decryption, and aggregation.

[0033] like Figure 2 As shown, in step 2, at time 0, the report is generated. After each smart meter generates a report, it transmits the report to the gateway. Step 2.1: Smart meter SM1 generates electricity consumption data (1, 2, 3) at time 0, and smart meter SM2 generates electricity consumption data (2, 4, 6) at time 0; Step 2.2, the smart meter sm1 generates the polynomial f(x)=1+2x+3x^2+x^3, and generates the corresponding shared secret set {f(1),f(2),f(3),f(4)}={7,25,61,121}; The smart meter sm2 generates the polynomial f(x)=2+4x+6x^2+2x^3, and generates the corresponding shared secret set {f(1),f(2),f(3),f(4)}={14,50,122,242}; Step 2.3: Smart meters sm1 and sm2 store the corresponding f(1) locally and send their respective {f(2), f(3), f(4)} to the gateway GW. Since f(1) is stored locally and not sent, no entity can infer the electricity consumption data without f(1), which is the basis for the algorithm.

[0034] Step 3: After receiving the reports generated by each smart meter at time 0, the gateway aggregates them and finally transmits the aggregated report to the control center. Specifically, the gateway GW receives the set {25,61,121} from smart meter sm1 and the set {50,122,242} from smart meter sm2. It adds the elements of the two sets to obtain the set {25+50,61+122,121+242}={75,183,363}, and transmits this set to the control center CC. This step utilizes the additivity property of Shamir's secret sharing to perform statistical aggregation of users' electricity consumption data.

[0035] Step 4: The control center (CC) receives the data set and stores it locally. Since the current time is less than the delay parameter k, the decryption stage is not performed, and the data transmission proceeds to the next time step.

[0036] Specifically, after the control center CC receives the set {75,183,363}, it stores it locally. The transmission at time 0 ends. Since the time at this moment is less than the delay parameter k, the transmission at the next moment begins.

[0037] Step 5, Report generation at time 1: After each smart meter generates a report, it transmits the report to the gateway, which is the same as step 2. Step 5.1, smart meter sm1 generates electricity consumption data (1, 1, 1) at time 1, and sm2 generates electricity consumption data (2, 2, 2) at time 1; Step 5.2, the smart meter sm1 generates the polynomial f(x)=1+x+x^2+3x^3, and generates the corresponding shared secret set {f(1),f(2),f(3),f(4)}={6,31,94,213}; The smart meter sm2 generates the polynomial f(x) = 2 + 2x + 2x^2 + x^3, and generates the corresponding shared secret set {f(1), f(2), f(3), f(4)} = {7, 22, 53, 106}. Step 5.3: Smart meters sm1 and sm2 save the corresponding f(1) locally and send their respective {f(2), f(3), f(4)} to the gateway GW.

[0038] Step 6: After receiving the reports generated by each smart meter at time 1, the gateway GW aggregates them and finally transmits the aggregated report to the control center, which is the same as step 3. Specifically, the gateway GW receives the set {31,94,294} from smart meter sm1 and the set {22,53,104} from smart meter sm2, and adds the elements of the two sets to obtain the set; {31+22,94+53,213+106}={53,147,319}, and transmit this set to the control center CC.

[0039] Step 7: The control center (CC) receives the data set and stores it locally. Since the time at this point is not less than the delay parameter k, the data decryption stage begins. Specifically, after the control center CC receives the set {53,147, 319}, it stores it locally. The transmission at time 1 ends. Since the time at this point is not less than the delay parameter k, it proceeds to step 8, data decryption.

[0040] Step 8: The control center (CC) decrypts the data.

[0041] Step 8.1: The control center (CC) sends a data decryption request to the smart meter. The smart meter sm1 aggregates the data {7,6} stored locally at times 0 and 1 using the Chinese Remainder Theorem, obtaining m = 7*9*59 + 6*49*53 mod 3127 = 537. Then, m is encrypted using the public key to obtain the ciphertext. c=; The smart meter sm2 aggregates the locally stored data {14,7} from times 0 and 1 using the Chinese Remainder Theorem, obtaining m = 14*9*59 + 7*49*53 mod 3127 = 597. Then, it encrypts m using the public key to obtain the ciphertext: c=. Smart meters SM1 and SM2 each transmit their encrypted data to GW. This step aggregates and encrypts the data not transmitted in steps 2 and 5, thus achieving multi-dimensional data processing across multiple time points with a single aggregation and encryption operation.

[0042] Step 8.2, the gateway (GW) adds the received ciphertexts together to obtain the new ciphertext: The encrypted message is transmitted to the control center (CC). Step 8.3: After receiving the ciphertext, the control center CC decrypts it using the private key to obtain the plaintext 1134. It calculates that the sum of f(1) of smart meters sm1 and sm2 at time 0 is 1134 mod 53 = 21, and the sum of f(1) of smart meters sm1 and sm2 at time 1 is 1134 mod 59 = 13. It adds these to the sets of time 0 and time 1 stored locally to obtain the complete secret sharing set {21,75,183,363} at time 0 and the complete secret sharing set {13,53,147,319} at time 1. Step 8.4: The control center (CC) uses Lagrange interpolation to calculate the shared secret set at time 0, as shown in the figure below:

[0043] Based on the polynomial coefficients, the aggregated electricity consumption data for time 0 is (3, 6, 9); similarly, the secret shared set for time 1 is calculated as follows:

[0044] The aggregated electricity consumption data at time 0 can be obtained as (3, 3, 3), which is consistent with the actual situation.

[0045] In step 8.1 of this invention, the smart meter at the user end only uses the Chinese Remainder Theorem to aggregate data once and the homomorphic encryption algorithm to encrypt data once, enabling the control center (CC) to obtain the statistical data of smart meters sm1 and sm2 at time 0 and at time 1. Existing research requires several times more encryption and aggregation, significantly reducing the computational overhead of the user-end smart meter. Furthermore, this process is secure; attackers cannot obtain real electricity consumption data without revealing the private key.

[0046] This invention also provides a privacy-preserving smart grid multi-dimensional data adaptive high-efficiency aggregation system, comprising: System initialization module: Used to initialize the control center, gateway, and smart meters in step 1, and generate bilinear pairing parameters. Generate the public key pk and private key sk parameters of the Paillier public-key cryptosystem, as well as the aggregation parameters required for generating the Chinese Remainder Theorem; Smart meter report generation module: used to transmit the report generated by the user-end smart meter to the gateway in step 2; The aggregation report generation module is used to realize the aggregation and batch verification of data after the gateway receives the user's report in step 3, generate an aggregation report, and transmit the aggregation report to the control center. Data decryption module: Used to decrypt the data after the control center receives the aggregation report in step 4.

[0047] This invention also provides a privacy-protected smart grid multi-dimensional data adaptive high-efficiency aggregation device, comprising: Memory: A computer program that stores the above-mentioned method for adaptive and efficient aggregation of multidimensional data in a smart grid that supports privacy protection, and is a computer-readable device; Processor: Used to implement the privacy-preserving smart grid multidimensional data adaptive and efficient aggregation method when executing the computer program.

[0048] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, enables the implementation of the aforementioned privacy-preserving smart grid multidimensional data adaptive and efficient aggregation method.

Claims

1. A method for adaptive and efficient aggregation of multidimensional data in smart grids that supports privacy protection, characterized in that: Includes the following steps: Step 1, Initialize the control center, gateway, and smart meters: Generate bilinear pairing parameters Generate the public key pk and private key sk parameters of the Paillier public-key cryptosystem, as well as the aggregation parameters required for generating the Chinese Remainder Theorem; Step 2, Report Generation: After the smart meter at the user end generates a report, it is transmitted to the gateway; The specific method for step 2 is as follows: user smart meters Electricity consumption data is collected at time t. Then generate Polynomial of degree: ,in It's a random number, from a smart meter. according to generate A set of coordinate points ,in Smart meters Will Local collections exist Smart meters Then use your own private key for Calculate signature , Will Passed to the gateway ; Step 3: After receiving the user's report, the gateway performs data aggregation and batch verification, generates an aggregation report, and transmits the aggregation report to the control center. Step 4: After receiving the aggregation report, the control center decrypts the data.

2. The method for adaptive and efficient aggregation of multi-dimensional data in a smart grid that supports privacy protection, as described in claim 1, is characterized in that: The specific method for step 1 is as follows: Control Center (CC) selects a large prime number. According to safety parameters Calling the bilinear parameter generation algorithm Generate bilinear pairing parameters Choose a safe hash function ; Control Center (CC) selects two prime numbers and ,satisfy ,calculate as well as Define function Select generator And calculate Then the public key is The private key is ; Control Center CC Selection For coprime prime numbers ,in It is the maximum number of delays. , ,in It is the upper limit of the first dimension of data. This is the maximum number of users that the gateway can connect to, calculated by the control center (CC). ,satisfy ,as well as and Control Center CC announced ; Smart meters Choose one Use it as your private key and calculate Each gateway selects one Use it as your private key and calculate Smart meters and gateways publish their respective calculated pairing parameters. and .

3. The method for adaptive and efficient aggregation of multidimensional data in a smart grid that supports privacy protection, as described in claim 2, is characterized in that: The specific method for step 3 is as follows: gateway First check user ID and time To check if it is correct, then verify if the following formula holds true: If the above formula is true, then the signature is valid, and its correctness is as follows: After verifying that the equation holds true, the gateway will sum up all the collected coordinate points to obtain: in, The gateway uses its own private key. for Calculate signature ,Will Transmitted to the control center (CC).

4. The method for adaptive and efficient aggregation of multidimensional data in a smart grid that supports privacy protection, as described in claim 3, is characterized in that: The specific method for step 4 is as follows: After receiving the aggregated data results from the gateway, the control center (CC) first checks the gateway. and time Check if it is correct, and then verify the validity of the signature according to the following formula. If the equation is true, the signature is valid, and the data is stored locally. When the control center (CC) needs to obtain aggregated data results or the delay count reaches... At that time, a query request is sent to the gateway (GW), which then forwards the query request to the smart meter at the user's end. Smart meters Multiple sets of coordinates stored locally at different times Using CRT aggregation: Next, smart meters plaintext encryption: in, , Smart meters Calculate signature Smart meters Will Passed to the gateway After confirming that the control center (CC) has successfully received the data, clear the coordinate set. ; gateway First check user ID and time To verify if it is correct, then check if the signature is valid using the following formula: After verifying that the equation is true and the signature is valid, multiply all the collected ciphertext together to obtain: The gateway uses its own private key for Calculate signature ,Will Transmitted to the control center (CC); After receiving the aggregation results from the gateway, the Control Center (CC) first checks the gateway. and time To verify if it is correct, then check if the signature is valid using the following formula: Once the equation is verified and the signature is valid, the data is sent to the smart meter. Inform the user that the data was successfully received, and then decrypt using the following formula. : in, According to the Chinese Remainder Theorem, the control center CC is obtained through the following formula. : The control center (CC) based on the data already received locally. The sum of multidimensional electricity consumption data at the corresponding time point is obtained using the Lagrange interpolation method of the following formula: Except for the last coefficient, the coefficient of each item is a sum of electricity consumption data in one dimension; when Without performing a delay operation, this algorithm can be transformed from a delayed algorithm to a real-time algorithm, i.e., a smart meter. Each time, a set of coordinate points is obtained. After encrypting the first piece of data, you can upload all the data directly.

5. A smart grid multi-dimensional data adaptive high-efficiency aggregation system supporting privacy protection, used to implement the method of claim 1, characterized in that: include: System initialization module: Used to initialize the control center, gateway, and smart meters, and generate bilinear pairing parameters. Generate the public key pk and private key sk parameters of the Paillier public-key cryptosystem, as well as the aggregation parameters required for generating the Chinese Remainder Theorem; Smart meter report generation module: used to generate reports from user-end smart meters and transmit them to the gateway; Aggregated report generation module: This module is used to aggregate and batch verify data after the gateway receives reports from the user end, generate an aggregated report, and transmit the aggregated report to the control center. Data decryption module: Used to decrypt data after the control center receives the aggregated report.

6. A smart grid multi-dimensional data adaptive high-efficiency aggregation device supporting privacy protection, characterized in that: include: Memory: A computer program for a privacy-preserving smart grid multidimensional data adaptive and efficient aggregation method as described in any one of claims 1-4, which is a computer-readable device; Processor: Used to implement the privacy-preserving smart grid multidimensional data adaptive and efficient aggregation method as described in any one of claims 1-4 when executing the computer program.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the privacy-preserving, multi-dimensional data adaptive and efficient aggregation method for smart grids as described in any one of claims 1-4.

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