Data privacy protection and data aggregation method between intelligent gas meters

Abstract

The invention provides a data privacy protection and data aggregation method and device between intelligent gas meters, computer equipment and a storage medium, and the method comprises the following steps: a control center configures system parameters for the intelligent gas meters, and generates secret keys for the intelligent gas meters; acquiring gas meter data from the intelligent gas meter and processing the gas meter data; carrying out verification and error detection on the processed gas meter data; aggregating the gas meter data after verification and error detection; the aggregated gas meter data result is decrypted and reconstructed; and performing function query and multi-party calculation on the decrypted and reconstructed gas meter data. According to the method and the device, data privacy protection is carried out, batch verification of user data secret shares is realized, the consistency and correctness of the secret shares in the distribution and transmission process are ensured, invalid secret share reports can be effectively detected and positioned, and thus the reliability of data aggregation is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular, to a method for data privacy protection and data aggregation between smart gas meters. Background Art

[0002] Data privacy protection of smart gas meters refers to a series of measures to protect user gas usage data (such as gas usage, time, location, etc.), including data encryption transmission, secure storage, access control, and user authorization management, to prevent data leakage, abuse, or malicious tampering, and to ensure user privacy and data security.

[0003] Although current data privacy protection technologies for smart gas meters provide strong privacy protection in theory, they still face a series of challenges in practical applications.

[0004] First, the computational complexity is high. When technologies such as homomorphic encryption and secure multi-party computing are run on resource-constrained devices such as smart gas meters, they may lead to inefficiency. Second, there is the problem of communication overhead. Privacy protection protocols may increase additional information exchange during data transmission, affecting the response speed and real-time performance of the system.

[0005] In addition, the existing technology needs to be improved in terms of data aggregation efficiency, especially when processing large-scale data, more efficient algorithms are needed to meet the needs of real-time monitoring and rapid decision-making. The lack of error detection and recovery mechanisms means that once an error occurs during data transmission, the system may lack the ability to quickly locate and correct the problem. Summary of the invention

[0006] The purpose of the embodiment of the present application is, on the one hand, to provide a data privacy protection and data aggregation method between smart gas meters, so as to propose a verifiable privacy protection data aggregation method between smart gas meters, which adopts the following technical solution, including:

[0007] The control center configures system parameters for the smart gas meter and generates a key for the smart gas meter;

[0008] Acquire gas meter data from the smart gas meter and process the gas meter data;

[0009] Verifying and detecting errors in the processed gas meter data;

[0010] aggregating the gas meter data after verification and error detection;

[0011] Decrypting and reconstructing the aggregated gas meter data results;

[0012] Function query and multi-party computing are performed on the decrypted and reconstructed gas meter data.

[0013] Preferably, the control center configures system parameters for the smart gas meter, and the step of generating a key for the smart gas meter specifically includes:

[0014] The control center sets a threshold t for each server;

[0015] The control center assigns an identity index j to each server, where j=1, 2, ..., n, and n is the number of servers;

[0016] Setting the security parameter κ, the control center generates a cyclic group G of prime order p, where g is the generator of G;

[0017] Choose a cryptographic hash function H such that H:{0,1} * →{0,1} k ;

[0018] Generate a key for the smart gas meter Where N represents the number of smart gas meters controlled by the edge node, and the data of each smart gas meter is represented by SM i ,i=1,2,…,N, the generated key is expressed as The edge node generates the corresponding public key pair Each smart gas meter generates its public key pair

[0019] Preferably, the step of acquiring gas meter data from the smart gas meter and processing the gas meter data specifically includes:

[0020] Choose a random polynomial a i (x) = u0 + u1x + u2x 2 +…+u t-1 x t-1 mod p, b(x)=v0+v1x+v2x 2 +…+v t- 1x t-1 mod p, where u r ,v r ∈ R Z n ,r=0,1,…,t-1;

[0021] divide a i (0) = m i , for each share, the smart gas meter data SM i The coefficients of the generator polynomial are committed to:

[0022] Smart gas meter dataSM i Commit the coefficients of the polynomial Send to edge nodes;

[0023] Smart gas meter dataSM i Generate m i and a random number rand i The secret share [m i ] j =a i (j), [rand i ] j =b i (j), j = 1, 2, ..., n, and use the ElGamal cryptosystem to encrypt the secret share: C i,j =(c 1,i,j ,c 2,i,j ,c 3,i,j ), where k i ∈ R Z p ,

[0024] Preferably, the step of verifying and detecting errors in the processed gas meter data specifically includes:

[0025] The control center sends the data set S of the smart gas meter to the edge node;

[0026] After receiving the data, the edge node will verify the commitment in batches.

[0027] Preferably, the step of aggregating the gas meter data after verification and error detection specifically includes:

[0028] Summarize and integrate the gas meter data after verification and error detection according to specific rules or dimensions;

[0029] The gas meter data after being aggregated and integrated is verified.

[0030] Preferably, the step of decrypting and reconstructing the aggregated gas meter data result specifically includes:

[0031] The edge node sends the detection report of the smart gas meter to the cloud server;

[0032] The cloud server stores the encrypted test reports;

[0033] Edge node aggregated smart gas meter encrypted data secret share in,

[0034] The edge node will Send to the control center and the corresponding cloud server;

[0035] Decrypt to get the share:

[0036] Verify in the control center: If the verification fails, the cloud service is terminated, otherwise the next action is executed;

[0037] The control center calculates ∑ i∈S m i =M S =∑ i∈Q [M S ] i λ Q,i To reconstruct the aggregation result, Q is the set of cloud servers.

[0038] Preferably, the step of performing function query and multi-party calculation on the decrypted and reconstructed gas meter data specifically includes:

[0039] Selecting a query function according to the type, scope and query purpose of the gas meter data;

[0040] According to the selected query function, query the gas meter data to obtain the required query results;

[0041] Multi-party calculations are performed based on the purpose of the calculation, the parties involved, the data scope, and the calculation rules.

[0042] In order to solve the above technical problems, the present invention also provides a data privacy protection and data aggregation device between smart gas meters, which adopts the following technical solutions, including:

[0043] A configuration module, used for the control center to configure system parameters for the smart gas meter and generate a key for the smart gas meter;

[0044] A processing module, used for acquiring gas meter data from the smart gas meter and processing the gas meter data;

[0045] A verification and error detection module, used for verifying and detecting errors of the processed gas meter data;

[0046] an aggregation module, used for aggregating the gas meter data after verification and error detection;

[0047] A decryption and reconstruction module, used for decrypting and reconstructing the aggregated gas meter data result;

[0048] The computing module is used to perform function query and multi-party computing on the gas meter data after decryption and reconstruction.

[0049] In order to solve the above technical problems, the present invention also provides a computer device, which adopts the technical solution described below, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of the above-mentioned data privacy protection and data aggregation method between smart gas meters.

[0050] In order to solve the above technical problems, the present invention also provides a computer-readable storage medium, which adopts the technical solution described below, and stores computer-readable instructions on the computer-readable storage medium. When the computer-readable instructions are executed by the processor, the steps of the data privacy protection and data aggregation method between the above-mentioned smart gas meters are implemented.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] (1) Data privacy protection: A practical public key encryption scheme encrypts the data collected by the smart gas meter, ensuring the privacy of user data. Even during data transmission, unauthorized third parties cannot obtain the user's sensitive information;

[0053] (2) Batch verification of user data secret shares is achieved, ensuring the consistency and correctness of secret shares during distribution and transmission. This provides additional security against tampering of secret shares;

[0054] (3) Using cloud servers for secure multi-party computing (MPC) allows control centers and users to perform complex function calculations on aggregated data, such as statistical analysis and prediction, without leaking personal data;

[0055] (4) A fast error detection algorithm based on tree structure is proposed, which can effectively detect and locate invalid secret share reports, thereby improving the reliability of data aggregation. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A flow chart of a data privacy protection and data aggregation method between smart gas meters provided in an embodiment of the present application;

[0058] Figure 2It is a structural schematic diagram of an embodiment of a data privacy protection and data aggregation device between smart gas meters of the present invention;

[0059] Figure 3 It is a schematic structural diagram of an embodiment of a computer device of the present invention. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0061] The following embodiments are implemented by an architecture including smart gas meters, edge nodes, and cloud servers, and also involve execution by a control center, etc. Smart gas meters are installed in various residences, industrial plants, commercial markets, etc., forming home networks, industrial area networks, commercial area networks, etc. Edge nodes are lightweight servers deployed at the edge of the network, with certain storage and computing capabilities. Cloud servers are remote servers with powerful computing and storage capabilities. The control center is an authoritative organization that analyzes user smart gas meter data, such as oil and gas companies, etc.

[0062] Embodiment 1

[0063] Please refer to Figure 1 , Figure 1 A flowchart of an embodiment of a data privacy protection and data aggregation method between smart gas meters provided in an embodiment of the present application. The data privacy protection and data aggregation method between smart gas meters includes the following steps:

[0064] Step S1: The control center configures system parameters for the smart gas meter and generates a key for the smart gas meter.

[0065] In this embodiment, in step S1, the control center configures system parameters for the smart gas meter, and generates a key for the smart gas meter, which may specifically include the following steps:

[0066] S11, the control center sets a threshold t for each server.

[0067] The control center generally selects multiple servers to participate in the system operation and sets a threshold t for each server to ensure the correctness and security of the calculation.

[0068] S12, the control center assigns an identity index j to each server, where j = 1, 2, ..., n, and n is the number of servers.

[0069] The identity index j is unique, ensuring that each server's interactions and operations in the system can be accurately identified and tracked.

[0070] S13, setting the security parameter κ, the control center generates a cyclic group G of prime order p, where |p|=κ○g, g is the generator of G.

[0071] At the same time, let h∈R. G * Represents a random element.

[0072] S14, select a cryptographic hash function H such that H:{0,1} * →{0,1} k .

[0073] Therefore, the system parameters of the smart gas meter configuration are: κ, p, G, g, h, H, t, etc.

[0074] S15, Generate key for smart gas meter Where N represents the number of smart gas meters controlled by the edge node, and the data of each smart gas meter is represented by SM i ,i=1,2,…,N, the generated key is expressed as The edge node generates the corresponding public key pair Each smart gas meter generates its public key pair

[0075] The control center issues a timestamp T in each time period, and each collection time period may be, for example, 15 minutes. The specific collection time may be determined according to actual needs, and the collection time is not limited again.

[0076] Step S2, obtaining gas meter data from the smart gas meter and processing the gas meter data.

[0077] In this embodiment, step S2, obtaining gas meter data from the smart gas meter and processing the gas meter data may further include the following steps:

[0078] S21, select a random polynomial a i (x) = u0 + u1x + u2x 2 +…+u t-1 x t-1 mod p,

[0079] b(x)=v0+v1x+v2x 2 +…+v t-1 x t-1 mod p, where u r ,v r ∈ R Z n ,r=

[0080] 0,1,…,t-1.

[0081] S22, except a i (0) = mi , for each share, the smart gas meter data SM i The coefficients of the generator polynomial are committed to:

[0082] S23, Smart gas meter data SM i Commit the coefficients of the polynomial Sent to edge nodes.

[0083] S24, Smart gas meter data SM i Generate m i and a random number rand i The secret share [m i ] j =a i (j), [rand i ] j =b i (j), j = 1, 2, ..., n, and use the ElGamal cryptosystem to encrypt the secret share: C i,j =(c 1,i,j ,c 2,i,j ,c 3,i,j ), where k i ∈ R Z p ,

[0084] Through step S2, SM i Send within a specified time period to the edge node.

[0085] Step S3, verifying and detecting errors on the processed gas meter data.

[0086] In this embodiment, step S3, verifying and detecting errors on the processed gas meter data, may further include the following steps:

[0087] S31, the control center sends the data set S of the smart gas meter to the edge node.

[0088] S32, the edge node performs batch verification commitment after receiving.

[0089] The edge nodes efficiently verify the commitments of the smart gas meters in the dataset S in batches. Here, a data batch verification algorithm is proposed. The edge nodes first aggregate the commitments:

[0090]

[0091] For the homomorphism of secret shares, we can get:

[0092]

[0093] in

[0094] Whether the edge node verifies consistency: judgment and Are they equal?

[0095] Step S4, aggregating the gas meter data after verification and error detection.

[0096] In this embodiment, step S4, aggregating the gas meter data after verification and error detection may further include the following steps:

[0097] S41, summarizes and integrates the gas meter data after verification and error detection according to specific rules or dimensions.

[0098] After all commitments are verified, the verified and error-checked gas meter data is aggregated and integrated according to specific rules or dimensions. For example, data can be aggregated according to dimensions such as time (such as day, week, month) or space (such as region, user).

[0099] Through data aggregation, it is easier to identify patterns and trends in the data, providing a basis for subsequent analysis and decision-making.

[0100] S42, verifying the summarized and integrated gas meter data.

[0101] Data verification is an important step to ensure the accuracy and consistency of data aggregation results. This includes verifying the integrity, consistency, and uniqueness of the data. For example, you can check whether the data is missing, duplicated, or contradictory.

[0102] The purpose of verifying the summarized and integrated gas meter data is to improve the reliability of the data aggregation results and provide a solid foundation for subsequent analysis and decision-making.

[0103] Step S5, decrypting and reconstructing the aggregated gas meter data results.

[0104] In this embodiment, step S5, decrypting and reconstructing the aggregated gas meter data result may further include the following steps:

[0105] S51, the edge node sends the detection report of the smart gas meter to the cloud server.

[0106] S52, the cloud server stores the encrypted detection report.

[0107] S53, Secret share of encrypted data of smart gas meter aggregated by edge node in,

[0108] S54, the edge node will Sent to the control center and the corresponding cloud server.

[0109] S55, decrypt to get the share:

[0110] After receiving the data aggregation query from the control center, the cloud servers send their aggregated result shares and committed shares to the control center.

[0111] S56, the control center verifies: If the verification fails, the cloud service terminates, otherwise the next action is executed.

[0112] S57, the control center calculates ∑ i∈S m i =M S =∑ i∈Q [M S ] i λ Q,i To reconstruct the aggregation result, Q is the set of cloud servers.

[0113] The purpose of decryption and reconstruction is to ensure the authenticity and security of data. As an important instrument for measuring gas energy, the secure transmission and accurate analysis of data of smart gas meters are crucial to ensure the stability and security of gas supply. Decryption can ensure that the integrity of data is not destroyed and prevent malicious tampering or theft. At the same time, the reconstruction process can format and standardize the data to improve the readability and comprehensibility of the data.

[0114] Decryption and reconstruction help to achieve efficient use of data. By decrypting and reconstructing smart gas meter data, it can be converted into more valuable information resources. These data can be used for gas company operation analysis, user behavior analysis, and market forecasting, providing more accurate data support for corporate decision-making.

[0115] Decryption and reconstruction can also help improve the intelligence level of gas services. Smart gas meters have functions such as remote monitoring and real-time warning. Through the decryption and reconstruction of data, these functions can be further optimized and upgraded. For example, real-time monitoring and early warning of users' gas usage can be achieved, potential safety hazards can be discovered and resolved in a timely manner, and the safety and reliability of gas services can be improved.

[0116] Step S6, performing function query and multi-party computing on the decrypted and reconstructed gas meter data.

[0117] In this embodiment, step S6, performing function query and multi-party computing on the decrypted and reconstructed gas meter data may further include the following steps:

[0118] S61, selecting a query function according to the type, range and query purpose of the gas meter data.

[0119] Such as SUM (sum), AVG (average), MAX (maximum value), MIN (minimum value), etc.

[0120] S62, performing a query operation on the gas meter data according to the selected query function to obtain the required query result.

[0121] In a data processing tool or programming environment, execute the query function and obtain the query results.

[0122] S63, perform multi-party calculation according to the purpose of calculation, participants, data scope and calculation rules.

[0123] Under the premise of ensuring data security, data sharing among multiple parties can be realized, and data verification can be performed to ensure the accuracy and consistency of the data. Encryption technology, data desensitization or data partitioning can be used to achieve data sharing and verification.

[0124] Utilize technologies such as distributed computing, cloud computing or blockchain to achieve collaborative computing and result aggregation among multiple parties.

[0125] After multi-party computation, the results of multi-party computation can also be verified to ensure their accuracy and reliability. For example, the results can be verified by comparing the computation results with the expected results, checking for anomalies and errors in the computation process, etc.

[0126] Apply the verified results to actual business scenarios, such as energy management, fee settlement, user behavior analysis, etc. According to the specific application scenarios and needs, convert the calculation results into actionable suggestions or decision-making basis.

[0127] Implementing this embodiment has the following beneficial effects:

[0128] (1) Data privacy protection: A practical public key encryption scheme encrypts the data collected by the smart gas meter, ensuring the privacy of user data. Even during data transmission, unauthorized third parties cannot obtain the user's sensitive information;

[0129] (2) Batch verification of user data secret shares is achieved, ensuring the consistency and correctness of secret shares during distribution and transmission. This provides additional security against tampering of secret shares;

[0130] (3) Using cloud servers for secure multi-party computing (MPC) allows control centers and users to perform complex function calculations on aggregated data, such as statistical analysis and prediction, without leaking personal data;

[0131] (4) A fast error detection algorithm based on tree structure is proposed, which can effectively detect and locate invalid secret share reports, thereby improving the reliability of data aggregation.

[0132] Embodiment 2

[0133] Based on the same inventive concept, the present application also provides a data privacy protection and data aggregation device between smart gas meters. Figure 2 , as a response to the above Figure 1 The present invention provides an embodiment of a data privacy protection and data aggregation device between smart gas meters. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0134] like Figure 2 As shown, the data privacy protection and data aggregation device 70 between smart gas meters in this embodiment includes: a configuration module 71, a processing module 72, a verification and error detection module 73, an aggregation module 74, a decryption and reconstruction module 75 and a calculation module 76. Among them:

[0135] Configuration module 71, used for the control center to configure system parameters for the smart gas meter and generate a key for the smart gas meter;

[0136] A processing module 72, used for acquiring gas meter data from the smart gas meter and processing the gas meter data;

[0137] A verification and error detection module 73, used for verifying and detecting errors in the processed gas meter data;

[0138] Aggregation module 74, for aggregating gas meter data after verification and error detection;

[0139] A decryption and reconstruction module 75, used for decrypting and reconstructing the aggregated gas meter data results;

[0140] The calculation module 76 is used to perform function query and multi-party calculation on the decrypted and reconstructed gas meter data.

[0141] Implementing this embodiment has the following beneficial effects:

[0142] (1) Data privacy protection: A practical public key encryption scheme encrypts the data collected by the smart gas meter, ensuring the privacy of user data. Even during data transmission, unauthorized third parties cannot obtain the user's sensitive information;

[0143] (2) Batch verification of user data secret shares is achieved, ensuring the consistency and correctness of secret shares during distribution and transmission. This provides additional security against tampering of secret shares;

[0144] (3) Using cloud servers for secure multi-party computing (MPC) allows control centers and users to perform complex function calculations on aggregated data, such as statistical analysis and prediction, without leaking personal data;

[0145] (4) A fast error detection device based on a tree structure is proposed, which can effectively detect and locate invalid secret share reports, thereby improving the reliability of data aggregation.

[0146] Embodiment 3

[0147] To solve the above technical problems, the embodiment of the present invention also provides a computer device. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0148] The above-mentioned computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 8 having components of a memory 81, a processor 82, and a network interface 83, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0149] The computer device may be a desktop computer, a notebook computer, a PDA, a cloud server, etc. The computer device may interact with the user through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

[0150] The memory 81 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 81 may be an internal storage unit of the computer device 8, such as a hard disk or memory of the computer device 8. In other embodiments, the memory 81 may also be an external storage device of the computer device 8, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 8. Of course, the memory 81 may also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 81 is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions for data privacy protection and data aggregation methods between smart gas meters. In addition, the memory 81 can also be used to temporarily store various types of data that have been output or are to be output.

[0151] In some embodiments, the processor 82 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 82 is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to run computer-readable instructions stored in the memory 81 or process data, such as computer-readable instructions for running the data privacy protection and data aggregation method between the smart gas meters.

[0152] The network interface 83 may include a wireless network interface or a wired network interface. The network interface 83 is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0153] Implementing this embodiment has the following beneficial effects:

[0154] (1) Data privacy protection: A practical public key encryption scheme encrypts the data collected by the smart gas meter, ensuring the privacy of user data. Even during data transmission, unauthorized third parties cannot obtain the user's sensitive information;

[0155] (2) Batch verification of user data secret shares is achieved, ensuring the consistency and correctness of secret shares during distribution and transmission. This provides additional security against tampering of secret shares;

[0156] (3) Using cloud servers for secure multi-party computing (MPC) allows control centers and users to perform complex function calculations on aggregated data, such as statistical analysis and prediction, without leaking personal data;

[0157] (4) A fast error detection device based on a tree structure is proposed, which can effectively detect and locate invalid secret share reports, thereby improving the reliability of data aggregation.

[0158] Embodiment 4

[0159] The present invention also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the data privacy protection and data aggregation method between smart gas meters as described above.

[0160] Implementing this embodiment has the following beneficial effects:

[0161] (1) Data privacy protection: A practical public key encryption scheme encrypts the data collected by the smart gas meter, ensuring the privacy of user data. Even during data transmission, unauthorized third parties cannot obtain the user's sensitive information;

[0162] (2) Batch verification of user data secret shares is achieved, ensuring the consistency and correctness of secret shares during distribution and transmission. This provides additional security against tampering of secret shares;

[0163] (3) Using cloud servers for secure multi-party computing (MPC) allows control centers and users to perform complex function calculations on aggregated data, such as statistical analysis and prediction, without leaking personal data;

[0164] (4) A fast error detection device based on a tree structure is proposed, which can effectively detect and locate invalid secret share reports, thereby improving the reliability of data aggregation.

[0165] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0166] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0168] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM) random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0169] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0170] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data privacy protection and data aggregation method between smart gas meters, characterized in that: The steps include: The control center configures system parameters for the smart gas meter and generates a key for the smart gas meter; Acquire gas meter data from the smart gas meter and process the gas meter data; Verifying and detecting errors in the processed gas meter data; aggregating the gas meter data after verification and error detection; Decrypting and reconstructing the aggregated gas meter data results; Function query and multi-party computing are performed on the decrypted and reconstructed gas meter data.

2. The data privacy protection and data aggregation method between smart gas meters according to claim 1 is characterized in that: The control center configures system parameters for the smart gas meter, and the steps of generating a key for the smart gas meter specifically include: The control center sets a threshold t for each server; The control center assigns an identity index j to each server, where j=1, 2, ..., n, and n is the number of servers; Setting the security parameter κ, the control center generates a cyclic group G of prime order p, where g is the generator of G; Choose a cryptographic hash function H such that H:{0,1} * →{0,1} k ; Generate a key for the smart gas meter Where N represents the number of smart gas meters controlled by the edge node, and the data of each smart gas meter is represented by SM i ,i=1,2,…,N, the generated key is expressed as The edge node generates the corresponding public key pair Each smart gas meter generates its public key pair 3. The data privacy protection and data aggregation method between smart gas meters according to claim 2 is characterized in that: The step of acquiring gas meter data from the smart gas meter and processing the gas meter data specifically includes: Choose a random polynomial a i (x) = u0 + u1x + u2x 2 +…+u t-1 x y-1 mod p, b(x)=v0+v1x+v2x 2 +…+v t-1 x t- 1 mod p, where u r ,v r ∈ R Z n ,r=0,1,…,t-1; divide by a i (0) = m i , for each share, the smart gas meter data SM i The coefficients of the generator polynomial are committed to: Smart gas meter dataSM i Commit the coefficients of the polynomial Send to edge nodes; Smart gas meter dataSM i Generate m i and a random number rand i The secret share [m i ] j =a i (j), [rand i ] j =b i (j), j = 1, 2, ..., n, and use the ElGamal cryptosystem to encrypt the secret share: C i,j =(c 1,i,j ,c 2,i,j ,c 3,i,j ), where k i ∈ R Z p , 4. The data privacy protection and data aggregation method between smart gas meters according to claim 1 is characterized in that: The step of verifying and detecting errors on the processed gas meter data specifically includes: The control center sends the data set S of the smart gas meter to the edge node; After receiving the data, the edge node will verify the commitment in batches.

5. The data privacy protection and data aggregation method between smart gas meters according to claim 1 is characterized in that: The step of aggregating the gas meter data after verification and error detection specifically includes: Summarize and integrate the gas meter data after verification and error detection according to specific rules or dimensions; The gas meter data after being aggregated and integrated is verified.

6. The data privacy protection and data aggregation method between smart gas meters according to claim 3 is characterized in that: The step of decrypting and reconstructing the aggregated gas meter data result specifically includes: The edge node sends the detection report of the smart gas meter to the cloud server; The cloud server stores the encrypted test reports; Edge node aggregated smart gas meter encrypted data secret share in, The edge node will Send to the control center and the corresponding cloud server; Decrypt to get the share: Verify in the control center: If the verification fails, the cloud service is terminated, otherwise the next action is executed; The control center calculates ∑ i∈S m i =M S =∑ i∈Q [M S ] i λ Q,i To reconstruct the aggregation result, Q is the set of cloud servers.

7. The data privacy protection and data aggregation method between smart gas meters according to any one of claims 1 to 6, characterized in that: The step of performing function query and multi-party calculation on the decrypted and reconstructed gas meter data specifically includes: Selecting a query function according to the type, scope and query purpose of the gas meter data; According to the selected query function, query the gas meter data to obtain the required query results; Multi-party calculations are performed based on the purpose of the calculation, the parties involved, the data scope, and the calculation rules.

8. A data privacy protection and data aggregation device between smart gas meters, characterized in that: include: A configuration module, used for the control center to configure system parameters for the smart gas meter and generate a key for the smart gas meter; A processing module, used for acquiring gas meter data from the smart gas meter and processing the gas meter data; A verification and error detection module, used for verifying and detecting errors of the processed gas meter data; an aggregation module, used for aggregating the gas meter data after verification and error detection; A decryption and reconstruction module, used for decrypting and reconstructing the aggregated gas meter data result; The computing module is used to perform function query and multi-party computing on the gas meter data after decryption and reconstruction.

9. A computer device, comprising a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the data privacy protection and data aggregation method between smart gas meters as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data privacy protection and data aggregation method between smart gas meters according to any one of claims 1 to 7.

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