A power distribution network security protection method based on trusted computing and privacy computing

Abstract

The application discloses a power distribution network security protection method based on trusted computing and privacy computing, and comprises the following steps: deploying an aggregation layer at a local end, so that the local end comprises an edge node layer containing smart meters, an aggregation layer containing aggregators and a terminal layer containing terminals; regarding all smart meters, aggregators and terminal devices of the local end as network nodes; using trusted computing technology to realize the safety of the states of the nodes, the trusted interaction between layers and the trusted verification of the interaction between edge nodes, and establishing trusted communication between the nodes; and using homomorphic encryption technology to split and aggregate the power consumption data of users at the local end, so that the aggregators can only obtain the total power consumption of users in the region, and cannot obtain the power consumption of a single user, so as to realize security protection. The application can protect the privacy of the power consumption data of users.

Description

Technical Field

[0001] This invention relates to the technical field of electricity user data privacy protection and learning, and particularly to a distribution network security protection method based on trusted computing and privacy computing. Background Technology

[0002] Currently, the following issues are faced regarding user electricity information security:

[0003] In traditional power distribution networks, the security protection for edge devices such as smart meters is insufficient, which can easily lead to security problems such as data leakage and data tampering.

[0004] The electricity consumption data collected by smart meters and transmitted upstream is not encrypted by traditional power systems. This can lead to the risk of privacy leaks of user electricity consumption data during transmission and calculation. Summary of the Invention

[0005] The technical problem to be solved by this invention is that it proposes a distribution network security protection method based on trusted computing and privacy computing, which is used to improve the security of local side operation and interaction of distribution network and the degree of privacy protection of user electricity data.

[0006] To address the aforementioned technical problem, one aspect of the present invention provides a power distribution network security protection method based on trusted computing and privacy computing, which includes at least the following steps:

[0007] Step S10: Deploy the aggregation layer on the local end; so that the local end includes an edge node layer containing smart meters, an aggregation layer containing aggregators, and a terminal layer containing terminals.

[0008] Step S11: Treat all smart meters, aggregators, and terminal devices on the local end as network nodes; use trusted computing technology to ensure the security of the state of each node, the trustworthiness of interactions between layers, and the trustworthiness of interactions between edge nodes, and establish trusted communication between nodes.

[0009] Step S12: The local end combines homomorphic encryption technology to split and then aggregate the electricity consumption data of each user, so that the aggregator end can only obtain the total electricity consumption of users in the region, but not the electricity consumption of individual users, in order to achieve security protection.

[0010] Preferably, step S11 further includes:

[0011] Trust chains are built using trusted modules embedded in each node to achieve trust verification for each node itself, specifically including:

[0012] When a node starts up, the trusted module powers on before the CPU in the node. First, the trusted module performs an integrity measurement on the BIOS. After the measurement passes, it transfers the permissions to the BIOS. The BIOS then performs an integrity measurement on the operating system loader, and the operating system loader performs an integrity measurement on the operating system kernel. The integrity measurement values ​​are then stored in the trusted module's platform configuration register (PCR) according to the startup and running order, thus building a linear static trust chain.

[0013] Once the node's operating system kernel is running, the kernel schedules programs according to tasks, uses a metric agent to perform integrity measurements on the scheduled programs, and stores the metric values ​​in the platform configuration register of the trusted module, thereby building a star-shaped dynamic trust chain.

[0014] Preferably, step S11 further includes:

[0015] Ensuring trustworthy interactions between nodes through remote trusted proofs specifically includes:

[0016] Before the two nodes exchange information, they establish a channel that can only send and receive request information and integrity reports. The requesting party completes its own integrity measurement according to the integrity requirements of the responding party, forms an integrity report and signs it with its own private key, and then sends the signed integrity report to the responding party for verification.

[0017] The verification of the respondent is divided into identity verification and integrity metric verification. The respondent verifies the identity of the requester by using the other party's public key. If the identity is correct, the respondent verifies whether the integrity metric of the requester is consistent with the integrity metric stored in the platform configuration register in the local trusted module to verify whether the running state of the requester has been tampered with. If the verification is successful, the requester is considered trustworthy, and information exchange can then proceed.

[0018] Preferably, step S12 further includes:

[0019] Step S120: Each smart meter generates a random segmentation ratio based on the number of meters connected to the aggregator, and segments the user's total electricity consumption data over a period of time according to this segmentation ratio.

[0020] Step S121: The smart meter generates two sets of asymmetric keys: one set is a homomorphic key, including a homomorphic public key and a homomorphic private key; the other set is a normal signature key, including a non-homomorphic public key and a non-homomorphic private key; and sends the public key to the aggregator in the local terminal.

[0021] In step S122, each smart meter encodes its multiple electricity consumption data segments using different homomorphic public keys to form ciphertext; and sends the ciphertext of its own electricity consumption data segments to the aggregator.

[0022] In step S123, the aggregator sums the ciphertext data encrypted with the same homomorphic public key to obtain the aggregated ciphertext data, and sends it to the user holding the homomorphic public key.

[0023] In step S124, after receiving the summed ciphertext data, the user decrypts it with a homomorphic private key to obtain the plaintext summed from the electricity data fragments of the same location of each user; and then uses its own non-homomorphic private key to encrypt and sign the plaintext to obtain the signed ciphertext data, which is then sent to the aggregator.

[0024] In step S125, after receiving the encrypted data sent by each user, the aggregator uses the non-homomorphic public key of each user to verify their identity. After successful verification, the aggregator aggregates the data sent by multiple legitimate users to obtain the total electricity consumption data for the area.

[0025] Preferably, step S120 further includes:

[0026] Each smart meter generates a random segmentation ratio c1, c2, c3, ... c based on the number n meters connected to the aggregator. n And use the following formula (1) to divide the user's total electricity consumption data L over a period of time according to this segmentation ratio. i Perform cutting and segmentation:

[0027] L i =c1×L i +c2×L i +...+c n ×L i =L i1 +L i2 +...+L in ;i∈[1,n] (1)

[0028] The segmentation ratio satisfies the following constraint: 0 ≤ c1, c2, ... c n ≤1 and c1+c2+...c n =1.

[0029] Preferably, step S121 further includes:

[0030] The smart meter generates two sets of asymmetric keys, one of which is a homomorphic key, including: a homomorphic public key Pub. homo and homomorphic private key Pr i homo The other set is a standard signature key, including a non-homomorphic public key, Pub. nor Non-homomorphic private key Pr inor ; wherein, the homomorphic key adopts homomorphic encryption based on an elliptic curve encryption mechanism, and an elliptic curve base point G is given, wherein the homomorphic private key is a random number satisfying: Pr i homo <k, where k is the order of G; Pub homo =Pr i homo ·G.

[0031] Preferably, said step S122 further comprises:

[0032] The smart meter i encodes a plurality of fragmented power consumption data fragments L of itself ij to points on the defined elliptic curve Smart meter i then generates a random number r ij , calculates and combines them into a ciphertext and sends the ciphertext of its own power consumption data fragment to the aggregator;

[0033] Wherein, the ciphertext calculation formulas are shown as formulas (2), (3) and (4):

[0034]

[0035]

[0036]

[0037] Preferably, said step S123 further comprises:

[0038] The aggregator sums the ciphertext data encrypted with the same user's homomorphic public key to obtain aggregated ciphertext data D j ', wherein the calculation formula for the aggregated ciphertext data is shown as formula (5):

[0039]

[0040] The aggregator then sends the aggregated ciphertext data D j ' to the user corresponding to said public key.

[0041] Preferably, said step S124 further comprises:

[0042] After receiving the summed ciphertext data, the user decrypts it with the homomorphic private key Pri homo to obtain plaintext data D of column fragment summation j , wherein the calculation formula for the plaintext data of column fragment summation is shown as formula (6):

[0043]

[0044] The specific homomorphic decryption process is shown in equations (7), (8), and (9):

[0045]

[0046]

[0047] And Elliptic decoding yields D j :

[0048]

[0049] The user will use their own non-homogeneous private key Pri to decrypt the data. nor The encrypted data is then re-signed to obtain the signed ciphertext data E. j ', and send it to the aggregator; where the formula for calculating the signed ciphertext data is shown in Equation (10):

[0050]

[0051] Preferably, step S125 further includes:

[0052] The aggregator utilizes each user's non-homomorphic public key Pub nor To verify whether the data comes from a legitimate user, the aggregator calculates the encrypted data verification using the formula shown in equation (11):

[0053]

[0054] After user authentication is successful, the aggregator will process the data sent by multiple legitimate users. j The total electricity consumption data H is obtained by aggregation, and the formula for calculating the total electricity consumption data is shown in equation (12):

[0055]

[0056] Implementing the embodiments of the present invention has the following beneficial effects:

[0057] This invention provides a power distribution network security protection method based on trusted computing and privacy computing. Addressing the insufficient security protection of edge devices in traditional power distribution networks and the potential privacy leakage of user electricity consumption data, it constructs a protection system based on trusted computing technology at the local end, enabling each node to operate and interact in a secure and reliable environment. This solves the security problems such as data leakage and data tampering caused by insufficient security protection at the edge of traditional power distribution networks. Simultaneously, by utilizing a data splitting and aggregation method based on homomorphic encryption, the aggregator can only obtain the total electricity consumption of users within the region, not the electricity consumption of individual users, thus effectively solving the existing user privacy leakage problem and possessing significant practical value. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a schematic diagram of the main flow of an embodiment of a power distribution network security protection method based on trusted computing and privacy computing provided by the present invention;

[0060] Figure 2 This is a schematic diagram illustrating the application environment of the present invention;

[0061] Figure 3 This is a schematic diagram illustrating the principle of establishing trusted communication between nodes as per the present invention.

[0062] Figure 4 for Figure 1 A more detailed flowchart of step S12;

[0063] Figure 5 for Figure 4 A schematic diagram illustrating the principle of the corresponding edge-side user electricity data privacy protection encryption scheme based on homomorphic encryption. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0065] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0066] like Figure 1 The diagram shows the main flow of an embodiment of a power distribution network security protection method based on trusted computing and privacy computing provided by the present invention; combined with... Figures 2 to 5 As shown, in this embodiment, the method includes at least the following steps:

[0067] Step S10: Deploy the aggregation layer on the local end; making the local end include an edge node layer containing smart meters, an aggregation layer containing aggregators, and a terminal layer containing terminals; see reference Figure 2 As shown, in this embodiment, the distribution network layered structure is redefined as a four-layer structure consisting of a master station layer, a terminal layer, an aggregation layer, and an edge node layer. This architecture adds an aggregation layer to the existing structure and incorporates the need for interaction between smart meters, thereby forming a three-layer structure of edge node layer, aggregation layer, and terminal layer at the local end.

[0068] Step S11: Treat all smart meters, aggregators, and terminal devices on the local end as network nodes; use trusted computing technology to ensure the security of the state of each node, the trustworthiness of interactions between layers, and the trustworthiness of interactions between edge nodes, and establish trusted communication between nodes.

[0069] Understandably, this embodiment requires implementing a security monitoring mechanism from the upper layer to the lower layer to form a local trust chain, proposing a reliable security monitoring mechanism between smart meters. Utilizing trusted computing technology, it achieves the security of each node's own state, reliable interactions between layers, and reliable interactions between edge nodes, thereby meeting the security requirements for stable operation of the power distribution network.

[0070] Specifically, in a practical example, step S11 further includes:

[0071] Trust chains are built using trusted modules embedded in each node to achieve trust verification for each node itself, specifically including:

[0072] When a node starts up, the trusted module powers on before the CPU in the node. First, the trusted module performs an integrity measurement on the BIOS. After the measurement passes, it transfers the permissions to the BIOS. The BIOS then performs an integrity measurement on the operating system loader, and the operating system loader performs an integrity measurement on the operating system kernel. The integrity measurement values ​​are then stored in the trusted module's platform configuration register (PCR) according to the startup and running order, thus building a linear static trust chain.

[0073] Once the node's operating system kernel is running, the kernel schedules programs according to tasks, uses a metric agent to perform integrity measurements on the scheduled programs, and stores the metric values ​​in the platform configuration register of the trusted module, thereby building a star-shaped dynamic trust chain.

[0074] Meanwhile, in a practical example, step S11 further includes:

[0075] Ensuring trustworthy interactions between nodes through remote trusted proofs specifically includes:

[0076] Before the two nodes exchange information, they establish a channel that can only send and receive request information and integrity reports. The requesting party completes its own integrity measurement according to the integrity requirements of the responding party, forms an integrity report and signs it with its own private key, and then sends the signed integrity report to the responding party for verification.

[0077] The verification of the respondent is divided into identity verification and integrity metric verification. The respondent verifies the identity of the requester by using the other party's public key. If the identity is correct, the respondent verifies whether the integrity metric of the requester is consistent with the integrity metric stored in the platform configuration register in the local trusted module to verify whether the running state of the requester has been tampered with. If the verification is successful, the requester is considered trustworthy, and information exchange can then proceed.

[0078] Step S12: The local end combines homomorphic encryption technology to split and then aggregate the electricity consumption data of each user, so that the aggregator end can only obtain the total electricity consumption of users in the region, but not the electricity consumption of individual users, in order to achieve security protection.

[0079] More specifically, please refer to Figure 3 and Figure 4 As shown, step S12 further includes:

[0080] Step S120: Each smart meter generates a random segmentation ratio based on the number of meters connected to the aggregator, and segments the user's total electricity consumption data over a period of time according to this segmentation ratio.

[0081] Step S120 further includes:

[0082] Each smart meter generates a random segmentation ratio c1, c2, c3, ... c based on the number n meters connected to the aggregator. n And use the following formula (1) to divide the user's total electricity consumption data L over a period of time according to this segmentation ratio. i Perform cutting and segmentation:

[0083] L i =c1×L i +c2×L i +...+c n ×L i =L i1 +L i2 +...+L in ;i∈[1,n] (1)

[0084] The segmentation ratio satisfies the following constraint: 0 ≤ c1, c2, ... c n ≤1 and c1+c2+...c n =1.

[0085] Step S121: the smart meter generates two sets of asymmetric keys, one set is homomorphic key comprising a homomorphic public key and a homomorphic private key; the other set is a common signature key comprising a non-homomorphic public key and a non-homomorphic private key; and sends the public keys therein to an aggregator in a local end;

[0086] wherein step S121 further comprises:

[0087] the smart meter generates two sets of asymmetric keys, one set is a homomorphic key comprising: a homomorphic public key Pub homo and a homomorphic private key Pr i homo ; the other set is a common signature key comprising a non-homomorphic public key Pub nor and a non-homomorphic private key Pr i nor ; wherein the homomorphic key adopts homomorphic encryption based on an elliptic curve encryption mechanism, an elliptic curve base point G is given, and the homomorphic private key as a random number satisfies: Pr i homo <k, where k is the order of G; Pub homo =Pr i homo ·G.

[0088] Step S122: each smart meter encodes a plurality of fragmented power consumption data fragments of the smart meter by using different homomorphic public keys to form ciphertexts; and sends the ciphertexts of the power consumption data fragments of the smart meter to the aggregator;

[0089] wherein step S122 further comprises:

[0090] smart meter i encodes a plurality of fragmented power consumption data fragments L ij to points on a defined elliptic curve smart meter i further generates a random number r ij , calculates and combines the same into a ciphertext and sends the ciphertext of the power consumption data fragments of the smart meter to the aggregator;

[0091] wherein, the ciphertext calculation formula is shown as formulas (2), (3) and (4):

[0092]

[0093]

[0094]

[0095] Step S123: the aggregator sums ciphertext data encrypted by using the same homomorphic public key to obtain aggregated ciphertext data, and sends the aggregated ciphertext data to a user holding the homomorphic public key;

[0096] Step S123 further includes:

[0097] The aggregator sums the ciphertext data encrypted with the same user's homomorphic public key to obtain the aggregated ciphertext data D. j The formula for calculating the aggregated ciphertext data is shown in equation (5):

[0098]

[0099] The aggregator then combines the aggregated ciphertext data D j 'Sent to the user who holds the public key.'

[0100] In step S124, after receiving the summed ciphertext data, the user decrypts it with a homomorphic private key to obtain the plaintext summed from the electricity data fragments of the same location of each user; and then uses its own non-homomorphic private key to encrypt and sign the plaintext to obtain the signed ciphertext data, which is then sent to the aggregator.

[0101] Step S124 further includes:

[0102] After receiving the summed ciphertext data, the user uses the homomorphic private key Pr i homo Decryption yields the plaintext data D, which is the sum of column-directed fragments. j The formula for calculating the plaintext data by summing column segments is shown in equation (6):

[0103]

[0104] The specific homomorphic decryption process is shown in equations (7), (8), and (9):

[0105]

[0106]

[0107] And Elliptic decoding yields D j :

[0108]

[0109] The user will use their own non-homogeneous private key Pri to decrypt the data. nor The encrypted data is then re-signed to obtain the signed ciphertext data E. j ', and send it to the aggregator; where the formula for calculating the signed ciphertext data is shown in Equation (10):

[0110]

[0111] In step S125, after receiving the encrypted data sent by each user, the aggregator uses the non-homomorphic public key of each user to verify their identity. After successful verification, the aggregator aggregates the data sent by multiple legitimate users to obtain the total electricity consumption data for the area.

[0112] Step S125 further includes:

[0113] The aggregator utilizes each user's non-homomorphic public key Pub nor To verify whether the data comes from a legitimate user, the aggregator calculates the encrypted data verification using the formula shown in equation (11):

[0114]

[0115] After user authentication is successful, the aggregator will process the data sent by multiple legitimate users. j The total electricity consumption data H is obtained by aggregation, and the formula for calculating the total electricity consumption data is shown in equation (12):

[0116]

[0117] This allows us to obtain the total electricity consumption of all users connected to the aggregator at a given moment, but not the raw data, thus effectively protecting users' privacy.

[0118] Understandably, this invention, leveraging a mature trusted computing framework and cutting-edge homomorphic encryption privacy protection methods, firstly adds an aggregation layer to the original structure and incorporates interaction requirements between smart meters, thus forming a three-layer structure on the local end: an edge node layer, an aggregation layer, and a terminal layer. This method consists of two phases. The first phase utilizes trusted computing technology to construct a local-end security protection system. Both the aggregator and smart meters are treated as network nodes. Each node, based on its built-in trusted module, uses its integrity measurement to build a trust chain to ensure its own trustworthiness. Trusted interaction between nodes is achieved through remote trusted proof; that is, each node completes its own integrity measurement according to the requirements of the interacting node, generates an integrity report, signs it with its private key, and sends it to the node requiring interaction for trusted verification. Only after successful verification can trusted communication occur.

[0119] The second stage, based on homomorphic encryption, involves data splitting and aggregation. On the user side, each smart meter generates two pairs of asymmetric keys: one homomorphic and one non-homomorphic. The user's total electricity consumption data over a period of time is segmented according to random weights. Different segments of the electricity consumption data are encrypted using their respective user homomorphic public keys, and the ciphertext of each segment is sent to the aggregator. On the aggregator side, the aggregator aggregates the ciphertext of multiple electricity consumption data segments sent by various users using the same user's public key and sends the aggregated ciphertext data to the user holding that key. This user then uses their homomorphic private key to decrypt the data sent by the aggregator, obtaining the plaintext sum of the electricity consumption data segments at the same location from each user. This user then signs this plaintext data using their non-homomorphic private key and sends it to the aggregator. Upon receiving the ciphertext data from each user, the aggregator verifies the user's identity using their non-homomorphic public key. After successful verification, the aggregator aggregates the data from multiple legitimate users to obtain the total electricity consumption data for the area. This ensures that the aggregator can only obtain the total electricity consumption of users within the region, and not the electricity consumption of individual users, effectively protecting the privacy and security of user electricity consumption data.

[0120] Implementing the embodiments of the present invention has the following beneficial effects:

[0121] This invention provides a power distribution network security protection method based on trusted computing and privacy computing. Addressing the insufficient security protection of edge devices in traditional power distribution networks and the potential privacy leakage of user electricity consumption data, it constructs a protection system based on trusted computing technology at the local end, enabling each node to operate and interact in a secure and reliable environment. This solves the security problems such as data leakage and data tampering caused by insufficient security protection at the edge of traditional power distribution networks. Simultaneously, by utilizing a data splitting and aggregation method based on homomorphic encryption, the aggregator can only obtain the total electricity consumption of users within the region, not the electricity consumption of individual users, thus effectively solving the existing user privacy leakage problem and possessing significant practical value.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the claims of the present invention. Therefore, any equivalent changes or modifications made without departing from the spirit disclosed in the present invention should be included within the scope of the claims of the present invention.

Claims

1. A power distribution network security protection method based on trusted computing and privacy computing, characterized in that, It should include at least the following steps: Step S10: Deploy an aggregation layer on the local end, so that the local end includes an edge node layer containing smart meters, an aggregation layer containing aggregators, and a terminal layer containing terminals. Step S11: Treat all smart meters, aggregators, and terminal devices on the local end as network nodes; Trusted computing technology is used to ensure the security of the state of each node, the trustworthiness of interactions between layers and the trustworthiness of interactions between edge nodes, and to establish trusted communication between nodes. Step S12: The local end combines homomorphic encryption technology to split and then aggregate the electricity consumption data of each user, so that the aggregator end can only obtain the total electricity consumption of users in the region, but not the electricity consumption of individual users, in order to achieve security protection. Step S12 further includes: Step S120: Each smart meter generates a random segmentation ratio based on the number of meters connected to the aggregator, and segments the user's total electricity consumption data over a period of time according to this segmentation ratio. Step S121: The smart meter generates two sets of asymmetric keys: one set is a homomorphic key, including a homomorphic public key and a homomorphic private key; the other set is a normal signature key, including a non-homomorphic public key and a non-homomorphic private key; and sends the public key to the aggregator in the local terminal. In step S122, each smart meter encodes its multiple electricity consumption data segments using different homomorphic public keys to form ciphertext; and sends the ciphertext of its own electricity consumption data segments to the aggregator. In step S123, the aggregator sums the ciphertext data encrypted with the same homomorphic public key to obtain the aggregated ciphertext data, and sends it to the user holding the homomorphic public key. In step S124, after receiving the summed ciphertext data, the user decrypts it with a homomorphic private key to obtain the plaintext summed from the electricity data fragments of the same location of each user; and then uses its own non-homomorphic private key to encrypt and sign the plaintext to obtain the signed ciphertext data, which is then sent to the aggregator. Step S125: After receiving the encrypted data sent by each user, the aggregator uses the non-homomorphic public key of each user to verify their identity. After the verification is successful, the aggregator aggregates the data sent by multiple legitimate users to obtain the total electricity consumption data in the area. Step S120 further includes: Each smart meter generates a random segmentation ratio based on the number n of meters connected to the aggregator. And use the following formula (1) to divide the user's total electricity consumption data over a period of time according to this segmentation ratio. Perform cutting and segmentation: （1） The segmentation ratio satisfies the following constraints: and .

2. The method as described in claim 1, characterized in that, Step S11 further includes: Trust chains are built using trusted modules embedded in each node to achieve trust verification for each node itself, specifically including: When a node starts up, the trusted module powers on before the CPU in the node. First, the trusted module performs an integrity measurement on the BIOS. After the measurement passes, it transfers the permissions to the BIOS. The BIOS then performs an integrity measurement on the operating system loader. The operating system loader then performs an integrity measurement on the operating system kernel and stores the integrity measurement values ​​in the trusted module's platform configuration register according to the startup and running order, thereby building a linear static trust chain. Once the node's operating system kernel is running, the kernel schedules programs according to tasks, uses a metric agent to perform integrity measurements on the scheduled programs, and stores the metric values ​​in the platform configuration register of the trusted module, thereby building a star-shaped dynamic trust chain.

3. The method as described in claim 2, characterized in that, Step S11 further includes: Ensuring trustworthy interactions between nodes through remote trusted proofs specifically includes: Before the two nodes exchange information, they establish a channel that can only send and receive request information and integrity reports. The requesting party completes its own integrity measurement according to the integrity requirements of the responding party, forms an integrity report and signs it with its own private key, and then sends the signed integrity report to the responding party for verification. The verification of the respondent is divided into identity verification and integrity metric verification. The respondent verifies the identity of the requester by using the other party's public key. If the identity is correct, the respondent verifies whether the integrity metric of the requester is consistent with the integrity metric stored in the platform configuration register in the local trusted module to verify whether the running state of the requester has been tampered with. If the verification is successful, the requester is considered trustworthy, and information exchange can then proceed.

4. The method as described in claim 3, characterized in that, Step S121 further includes: The smart meter generates two sets of asymmetric keys, one of which is a homomorphic key, including: a homomorphic public key. and homomorphic private key The other set is a standard signature key, including a non-homomorphic public key. Non-homomorphic private key The homomorphic key employs homomorphic encryption based on elliptic curve cryptography, providing the elliptic curve base point G, where the homomorphic private key, as a random number, satisfies: , where k is the order of G; .

5. The method as described in claim 4, characterized in that, Step S122 further includes: The smart meter will divide its own data into multiple segments of electricity consumption data. Encoding to points on the defined elliptic curve The smart meter i generates random numbers again. Calculate and Combine them into ciphertext It then sends the encrypted data fragments of its own electricity consumption to the aggregator; in, , The formulas for calculating ciphertext are shown in equations (2), (3), and (4): （2） （3） （4）。 6. The method as described in claim 5, characterized in that, Step S123 further includes: The aggregator sums the ciphertext data encrypted with the same user's homomorphic public key to obtain the aggregated ciphertext data. The formula for calculating the aggregated ciphertext data is shown in equation (5): （5） The aggregator then aggregates the ciphertext data. Send it to the user who holds the public key.

7. The method as described in claim 6, characterized in that, Step S124 further includes: After receiving the summed ciphertext data, the user uses the homomorphic private key Decryption yields the plaintext data obtained by summing column segments. The formula for calculating the plaintext data by summing column segments is shown in equation (6): （6） The specific homomorphic decryption process is shown in equations (7), (8), and (9): （7） （8） And Elliptic decoding : （9） The user will use their own non-homogeneous private key to decrypt the data. Encrypt and then sign to obtain the signed ciphertext data. The data is then sent to the aggregator; the formula for calculating the signed ciphertext data is shown in equation (10): （10）。 8. The method as described in claim 7, characterized in that, Step S125 further includes: The aggregator utilizes the non-homomorphic public keys of each user. To verify whether the data comes from a legitimate user, the aggregator calculates the encrypted data verification using the formula shown in equation (11): （11） After user authentication is successful, the aggregator will send data from multiple legitimate users. The total electricity consumption data is obtained by aggregation. The formula for calculating total electricity consumption is shown in equation (12): （12）。

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