Data encryption transmission method, device and system

Abstract

The application provides a data encryption transmission method, device and system, the method comprises the following steps: obtaining a random disturbance value sent by a fog computing component, and performing disturbance processing based on the random disturbance value to obtain disturbance data; performing noise adding processing on user data based on the disturbance data to obtain noise-added user data; sending the noise-added user data to the fog computing component, and performing primary operation on the noise-added user data based on a preset processing logic to obtain a primary operation result; and performing secondary operation on the primary operation result based on the preset processing logic to obtain target operation data. The data encryption transmission method, device and system provided by the application ensure the encryption transmission of data and effectively avoid the information leakage problem in the data transmission process.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and in particular to a data encryption transmission method, apparatus and system. Background Technology

[0002] The Energy Internet is a network that integrates advanced power electronics, information technology, and intelligent management technology to interconnect a large number of energy nodes, such as power networks, oil networks, and natural gas networks, which consist of distributed energy harvesting devices, distributed energy storage devices, and various types of loads, in order to achieve bidirectional energy flow, exchange, and sharing.

[0003] Currently, data collection and processing services based on the energy internet help improve energy utilization, but the following problems exist in actual use: In the energy internet, user data is collected by a central node, which can observe the user's real data values ​​and thus provide data support for subsequent decision-making. However, if the central node is attacked or there are internal adversaries, there is a risk of leakage of user privacy data. Summary of the Invention

[0004] To address the problems in the prior art, the present invention provides a data encryption transmission method, apparatus, and system.

[0005] This invention provides a data encryption transmission method, comprising:

[0006] A random perturbation value sent by the fog computing component is obtained. The random perturbation value is encrypted based on a first preset key pair before being sent. The random perturbation value is decrypted based on the first preset key pair, and perturbation processing is performed based on the random perturbation value to obtain perturbation data. The user data is then noise-added based on the perturbation data to obtain noisy user data. The noisy user data is sent to the fog computing component. The noisy user data is encrypted based on a preset Paillier encryption key and a second preset key pair before being sent.

[0007] The noisy user data is decrypted based on the second preset key pair, and an initial operation is performed on the noisy user data based on the preset processing logic to obtain the initial operation result; the initial operation result is sent to the central computing component, and the initial operation result is encrypted based on the third preset key pair before being sent.

[0008] Based on the third preset key pair, the initial operation result is decrypted, and the initial operation result is then processed again based on preset processing logic to obtain the secondary operation result; based on the preset Paillier decryption key pair, the secondary operation result is decrypted to obtain the target operation data.

[0009] According to a data encryption transmission method provided by the present invention, the step of adding noise to user data based on the random perturbation value to obtain noisy user data specifically includes:

[0010] Obtain the optimal noise x i x follows a Laplace distribution with position parameter μ and degree parameter b i ~Laplace(μ, b);

[0011] The optimal noise x corresponding to the random perturbation value is obtained based on the Laplace parameters (μ, b). i The user data is then subjected to noise enhancement based on the optimal noise level to obtain noisy user data.

[0012] According to a data encryption transmission method provided by the present invention, the step of obtaining the optimal noise x i x follows a Laplace distribution with position parameter μ and degree parameter b i ~Laplace(μ, b), specifically includes:

[0013] Let the total utility function be V(b) = θH(b) + (1-θ)SNR(b), where H(b) represents the information entropy, SNR(b) represents the signal-to-noise ratio, and θ represents a non-negative number less than 1;

[0014] Let H(b) > γ, SNR(b) > γ, and the total utility function converge, to obtain the Laplace distribution x. i ~Laplace(μ, b), where ∈ represents the preset minimum information entropy and γ represents the preset minimum signal-to-noise ratio.

[0015] According to a data encryption transmission method provided by the present invention, the signal-to-noise ratio formula is:

[0016]

[0017] The formula for information entropy is:

[0018]

[0019] Where, σ s Represents the signal, σ N Indicates noise.

[0020] According to a data encryption transmission method provided by the present invention, the method further includes: each preset key pair includes a public key and a private key, and the public keys of the first preset key pair, the second preset key pair and the third preset key pair, the random perturbation value, the initial calculation result, the target calculation data and the Laplace parameter (μ, b) are stored in a blockchain ledger.

[0021] The present invention also provides a data encryption transmission device, comprising:

[0022] A data acquisition component is used to acquire random perturbation values ​​sent by a fog computing component. These random perturbation values ​​are encrypted using a first preset key pair before being sent. The random perturbation values ​​are then decrypted using the first preset key pair, and perturbation processing is performed on them to obtain perturbation data. Noise is added to user data based on the perturbation data to obtain noisy user data. The noisy user data is then sent to the fog computing component, and the noisy user data is encrypted using a preset Paillier encryption key and a second preset key pair before being sent.

[0023] The fog computing component is used to decrypt the noisy user data based on a second preset key pair, and perform an initial operation on the noisy user data based on a preset processing logic to obtain an initial operation result; the initial operation result is sent to the central computing component, and the initial operation result is encrypted based on a third preset key pair before being sent.

[0024] The central computing component is used to decrypt the initial calculation result based on a third preset key pair, and to perform a second calculation on the initial calculation result based on preset processing logic to obtain a second calculation result; and to decrypt the second calculation result based on a preset Paillier decryption key pair to obtain the target calculation data.

[0025] According to a data encryption transmission device provided by the present invention, the device further includes: a key center component, used to generate a Paillier encryption key and a Paillier decryption key, and send the Paillier encryption key to the data acquisition component and the fog computing component respectively, and send the Paillier decryption key to the central computing component.

[0026] According to a data encryption transmission device provided by the present invention, the key center component is further configured to: obtain the optimal noise x corresponding to the random perturbation value based on the Laplace parameter (μ, b). i The user data is then subjected to noise enhancement based on the optimal noise level to obtain noisy user data.

[0027] According to a data encryption transmission device provided by the present invention, the data acquisition component is further used to generate a first preset key pair, the fog computing component is further used to generate a second preset key pair, and the central computing component is further used to generate a third preset key pair.

[0028] This invention provides a blockchain-based encrypted data transmission system, comprising: a blockchain network and a data encrypted transmission device.

[0029] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the data encryption transmission methods described above.

[0030] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described data encryption transmission methods.

[0031] The present invention also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of any of the data encryption transmission methods described above.

[0032] The data encryption transmission method, apparatus, and system provided by this invention prevent the leakage of real data during transmission by adding noise to the transmitted data. At the same time, the security of the data transmission process is further improved by using a key pair composed of a public key and a private key to perform sequential encryption and decryption processes on adjacent components of the data transmission, achieving the purpose of preventing eavesdropping. Furthermore, the Paillier encryption measures are superimposed on this basis, which further enhances the security of the entire data transmission process, realizing multiple confidentiality processing in the data encryption transmission process. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is one of the flowcharts illustrating the data encryption transmission method provided by the present invention;

[0035] Figure 2 This is the second flowchart illustrating the data encryption transmission method provided by the present invention;

[0036] Figure 3 This is a schematic diagram of the data encryption transmission device provided by the present invention;

[0037] Figure 4 This is a schematic diagram of the data encryption transmission system provided by the present invention;

[0038] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0040] Figure 1 This is one of the flowcharts illustrating the data encryption transmission method provided by the present invention, such as... Figure 1 As shown, the method includes:

[0041] Step S110: Obtain a random perturbation value sent by the fog computing component. The random perturbation value is encrypted based on a first preset key pair before being sent. The random perturbation value is decrypted based on the first preset key pair, and perturbation processing is performed based on the random perturbation value to obtain perturbation data. The user data is noise-added based on the perturbation data to obtain noisy user data. The noisy user data is sent to the fog computing component. The noisy user data is encrypted based on a preset Paillier encryption key and a second preset key pair before being sent.

[0042] Step S120: Decrypt the noisy user data based on the second preset key pair, and perform an initial operation on the noisy user data based on the preset processing logic to obtain the initial operation result; send the initial operation result to the central computing component, and encrypt the initial operation result based on the third preset key pair before sending the initial operation result;

[0043] Step S130: Based on the third preset key pair, the initial operation result is decrypted, and the initial operation result is re-operated based on the preset processing logic to obtain the secondary operation result; based on the preset Paillier decryption key pair, the secondary operation result is decrypted to obtain the target operation data.

[0044] It should be noted that, for data encryption transmission, the commonly used method for encryption and decryption using key pairs is the RSA algorithm. The RSA algorithm typically involves generating an RSA key pair, one of which is a secret key kept by the user; the other is a public key, which can be publicly disclosed and even registered on a web server. The principle of RSA public-key cryptography is that, according to number theory, finding two large prime numbers is relatively simple, but factoring their product is extremely difficult. Therefore, the product can be made public as the encryption key. This invention does not limit the specific algorithm chosen, as long as it satisfies the requirement of achieving encryption and decryption between adjacent nodes in data transmission using key pairs. Paillier homomorphic encryption, on the other hand, allows operations performed on plaintext to be performed on ciphertext after homomorphic encryption. Generally, there are semi-homomorphic and fully homomorphic encryption algorithms. Paillier is a semi-homomorphic encryption algorithm that supports the homomorphism of addition operations.

[0045] The preset processing logic described here can be a simple summation operation or a complex calculation logic that relies on complex mathematical formulas. The specific processing logic used can be preset according to the actual needs of data processing.

[0046] The data encryption transmission method provided by this invention avoids the leakage of real data during transmission by adding noise to the transmitted data. At the same time, the security of the data transmission process is further improved by using a key pair composed of a public key and a private key to perform sequential encryption and decryption processes on adjacent components of the data transmission, achieving the purpose of preventing eavesdropping. Furthermore, the Paillier encryption measure is superimposed on this basis, which further enhances the security of the entire data transmission process, realizing multiple confidentiality processing in the data encryption transmission process.

[0047] According to the data encryption transmission method provided by the present invention, the step of adding noise to user data based on the random perturbation value to obtain noisy user data specifically includes:

[0048] Obtain the optimal noise x i x follows a Laplace distribution with position parameter μ and degree parameter b i ~Laplace(μ, b);

[0049] The optimal noise x corresponding to the random perturbation value is obtained based on the Laplace parameters (μ, b). i The user data is then subjected to noise enhancement based on the optimal noise level to obtain noisy user data.

[0050] The acquisition of optimal noise x i x follows a Laplace distribution with position parameter μ and degree parameter b i~Laplace(μ, b), specifically includes:

[0051] Let the total utility function be V(b) = θH(b) + (1-θ)SNR(b), where H(b) represents the information entropy, SNR(b) represents the signal-to-noise ratio, and θ represents a non-negative number less than 1;

[0052] Let H(b) > γ, SNR(b) > γ, and the total utility function converge, to obtain the Laplace distribution x. i ~Laplace(μ, b), where ∈ represents the preset minimum information entropy and γ represents the preset minimum signal-to-noise ratio.

[0053] The signal-to-noise ratio formula is:

[0054]

[0055] The formula for information entropy is:

[0056]

[0057] Where, σ s Represents the signal, σ N Indicates noise.

[0058] It should be noted that when adding noise to data, a suitable value needs to be ensured. This is because if the noise added to the transmitted data is too small, the purpose of encryption will not be achieved, while if the noise added to the transmitted data is too large, the original transmitted data will lose its true information and damage the transmitted data itself. Based on this, when encrypting data transmission, a balance between data privacy and utility needs to be met, that is, to achieve maximum encryption processing of the transmitted data without damaging the true information of the transmitted data.

[0059] Information entropy measures the degree of chaos in information. The essence of privacy protection is to increase the degree of chaos in a system. Therefore, a higher value indicates better privacy protection, and thus... This represents the minimum information entropy accepted by the user, i.e., the minimum level of privacy protection. A higher signal-to-noise ratio (SNR) indicates a higher amount of information contained. Therefore, let γ represent the minimum SNR received by the computing center, i.e., the lowest data utility, then SNR(b) > γ. Further, assign weights θ to the privacy and utility functions to indicate the degree of perception of both, resulting in the total utility function V(b) = θH(b) + (1-0)SNR(b).

[0060] The key center component calculates the Laplace parameter based on the collected perturbation values ​​from the data acquisition component, ensuring that it meets the following requirements. SNR(b) > γ, and the total utility function converges, so the Laplace parameter (μ, b) is obtained and sent to the data acquisition component.

[0061] The data encryption transmission method provided by this invention uses the contradictory parameters of information entropy and signal-to-noise ratio as the basis for setting the noise value and obtains a total utility function. The noise value corresponding to each transmitted data is obtained by the convergence of this function. Based on this, the maximum encryption processing of the transmitted data can be achieved without compromising the true information of the transmitted data.

[0062] According to the data encryption transmission method provided by the present invention, the method further includes: each preset key pair includes a public key and a private key, and the public keys of the first preset key pair, the second preset key pair and the third preset key pair, the random perturbation value, the initial calculation result, the target calculation data and the Laplace parameter (μ,b) are stored in the blockchain ledger.

[0063] The data encryption transmission method provided by this invention stores multiple key pairs, random perturbation values, initial calculation results, target calculation results, and Laplace parameters involved in the data transmission process in the blockchain ledger. This provides an effective verification method in the process of data traceability and credibility verification, and enables effective determination of responsibility and subsequent accountability in the event of disputes.

[0064] Figure 2 This is the second flowchart illustrating the data encryption transmission method provided by the present invention. Taking the collection and transmission process of electricity consumption data in the energy internet as an example, the technical solution expressed in the present invention is illustrated. Figure 2 As shown:

[0065] The participants in this program include:

[0066] A smart meter (SM) is a data collection device for users, used to collect users' electricity consumption data in real time and perform noise addition and encryption processing on the data, which is equivalent to the aforementioned data acquisition component.

[0067] The Key Center (KC) is used to generate the Paillier key and optimal Laplace noise parameters during the initialization phase, and is equivalent to the aforementioned Key Center component;

[0068] Fog computing nodes (Fog) perform regional-level data aggregation operations, which is equivalent to the aforementioned fog computing components;

[0069] The Computing Center (CC) performs data aggregation calculations for the entire power grid, which is equivalent to the aforementioned central computing component;

[0070] Blockchain networks (BC) are used for data storage and are similar to the aforementioned blockchain networks.

[0071] Step 1: The key center initializes the system and generates keys for system participants. The Paillier key for homomorphic encryption is generated as follows:

[0072] The key center randomly selects two large prime numbers m and n that satisfy gcd(mn, (m-1)(n-1)) = 1. This ensures that the two prime numbers have the same length.

[0073] Calculate N = mn and λ = 1cm(m-1, n-1), where 1cm represents the least common multiple.

[0074] Random selection Satisfy gcd(L(g) λ mod N 2 Let Z be an integer, and N be a set of integers, where Z represents an integer and the index indicates the number of elements in the set. Therefore, the Paillier homomorphic encryption key is (N, g), and the decryption key is (λ, μ), where μ = (L(g)). λ mod N 2 )) -1 mod N.

[0075] Step 2: The smart meter, key center, fog computing node, and computing center use the RSA algorithm to generate their respective public and private keys, and publicly store the public key in the blockchain ledger. The RSA key generation process is as follows:

[0076] The smart meter / key center / fog computing node / computing center selects two large prime numbers p and q (p≠q);

[0077] Calculate N = p × q respectively.

[0078] Choose an integer e, e satisfying And e and Coprime, thus obtaining the public key (e, N);

[0079] for The private key (d, N) is obtained by calculating d using the extended Euclidean algorithm.

[0080] This gives us the key pairs from all parties:

[0081] (PK SMi SK SMi ), (PK KC SK KC ), (PK F SK F ), (PK CC SK CC )

[0082] Step 3: To prevent smart meters from sending actual electricity consumption values ​​to the key center node, fog computing nodes generate perturbation values ​​to provide data disturbance to the smart meters. The fog computing node generates a random perturbation value 'a' (a > 0) and stores its hash in the blockchain ledger. The fog computing node obtains the public key of smart meter i from the blockchain ledger, encrypts 'a' using smart meter i's public key, and sends it to the corresponding smart meter i, where E(a) = a. e mod N. After receiving the encrypted value, smart meter i uses its private key to decrypt it, recovering the plaintext a = E(a). d mod N, using a to represent the actual electricity consumption m collected by smart meter i. i The perturbation is performed, and the perturbation value y is obtained. i =m i +a. The smart meter i obtains the key center's public key through the blockchain and then uses the key center's public key to encrypt y. i And send it to the key center, E(y) i )=y i e modN.

[0083] Step 4: The key center collects E(y) from all smart meters i. i After decryption, Laplace-fitted noise parameters (μ, b) are generated for all perturbation values, where μ is the location parameter and b is the degree parameter. In this process, a privacy-utility trade-off is used to optimize the parameters, where the degree of privacy protection is measured by "information entropy" and the data utility is measured by "signal-to-noise ratio (SNR)".

[0084] Information entropy is represented as follows:

[0085]

[0086] Information entropy measures the degree of chaos in information. The essence of privacy protection is to increase the degree of chaos in a system. Therefore, a higher value indicates better privacy protection, and thus... This represents the minimum information entropy accepted by the user, i.e., the minimum level of privacy protection.

[0087] The signal-to-noise ratio is expressed as follows:

[0088]

[0089] Where, σ s 2 Let σ be the variance of the signal. N 2 Let σ be the variance of the noise. The Laplace distribution has a mean E(x) = μ and a variance σ. 2 =2b 2Therefore, the above formula can be further expressed as:

[0090]

[0091] A higher signal-to-noise ratio (SNR) indicates a higher amount of information contained. Therefore, let γ represent the minimum SNR received by the computing center, i.e. the lowest data utility, then SNR(b) > γ.

[0092] We further assign weights θ to the privacy and utility functions to indicate the degree of perception of both, and obtain the total utility function V(b) = θH(b) + (1-θ)SNR(b).

[0093] The key center calculates the Laplace parameter based on the collected perturbation values ​​of smart meters i, ensuring that it meets the following requirements. SNR(b)>γ, and the total utility function converges, so the Laplace parameter (μ, b) is obtained. After being encrypted with the public key of smart meter i, it is sent to smart meter i. The Laplace parameter (μ, b) is recorded in the blockchain ledger as data proof.

[0094] Step 5: After receiving the Laplace parameter, smart meter i decrypts it using its private key to obtain (μ, b). The Laplace parameter is based on the perturbation value y. i The calculation shows that, since the true values ​​of all smart meters i have been perturbed by a unit (a), their distribution map is shifted by a unit while maintaining its shape. Therefore, smart meters i can still use parameters (μ, b) to generate Laplace noise to add noise to their electricity consumption values. The noise x generated by smart meter i... i ~Laplace(μ,b), and thus obtain the power consumption value z with added noise. i =m i +x i .

[0095] Step 6: Since the noise-added value still reflects the actual electricity consumption to some extent, Paillier homomorphic encryption is used to further encrypt the noise-added value. Smart meter i uses the Paillier encryption key generated by the key center in the initial stage to encrypt z. i Homomorphic encryption is performed to obtain the ciphertext. Where r is a random number selected by smart meter i. The public key of the fog computing node is queried in the blockchain ledger and then applied to c. i Encrypted and sent to the fog computing node.

[0096] Step 7: Fog computing nodes are regional-level computing nodes, which can alleviate the computing pressure on the computing center and ensure that the computing center cannot obtain the electricity consumption value of individual smart meters i (the computing center has the Paillier decryption key, which can decrypt c). iAfter receiving a message from smart meter i, the fog computing node decrypts it using its private key to obtain c. i Based on Paillier additive homomorphic computation:

[0097]

[0098] in, In this context, 'i' represents the fog computing node number, and 'n' represents the number of smart meters 'i' in the area. The hash value is stored in the blockchain ledger as data proof. Fog computing nodes further query the computing center's public key in the blockchain ledger and... Encrypt the data and send it to the computing center.

[0099] Step 8: After receiving the message from the fog computing node, the computing center uses its private key to decrypt and obtain... Also based on Paillier additive homomorphic aggregation get Where n is the number of fog computing nodes. sum This is the aggregated value of the entire network's electricity consumption under Paillier encryption. The computing center uses its Paillier decryption key to decrypt this value.

[0100]

[0101] The final aggregated electricity consumption value is obtained. The value is then hashed and recorded in the blockchain ledger as data proof. During this process, even if there are irregularities at the computing center, the data is decrypted before aggregation. The result is still an aggregated value for a region, and it is impossible to obtain the actual electricity consumption value of an individual user, thus protecting user privacy.

[0102] The data encryption transmission method provided by this invention avoids sending real data to a central node for data computation. By adding noise and perturbation to the transmitted data, it prevents the leakage of real data. It balances privacy protection and data utility by selecting noise parameters to achieve data privacy protection that maintains data value. It avoids the key center generating and transmitting key pairs for each party. Instead, it uses a method where each blockchain node generates its own key and publishes its public key in the blockchain ledger, preventing security risks caused by attacks on the key center and eavesdropping on key transmission. Furthermore, through a blockchain-based data integrity verification protocol, it achieves data traceability, data authenticity and integrity verification, and stores the evidence data in the blockchain ledger.

[0103] Figure 3 This is a schematic diagram of the data encryption transmission device provided by the present invention, as shown below. Figure 3 As shown, the data encryption transmission device 300 includes:

[0104] The data acquisition component 310 is used to acquire random perturbation values ​​sent by the fog computing component. Before sending the random perturbation values, the random perturbation values ​​are encrypted using a first preset key pair. The random perturbation values ​​are then decrypted using the first preset key pair, and perturbation processing is performed based on the random perturbation values ​​to obtain perturbation data. Noise is added to user data based on the perturbation data to obtain noisy user data. The noisy user data is then sent to the fog computing component. Before sending the noisy user data, it is encrypted using a preset Paillier encryption key and a second preset key pair.

[0105] Fog computing component 320 is used to decrypt the noisy user data based on a second preset key pair, and perform an initial operation on the noisy user data based on preset processing logic to obtain an initial operation result; and send the initial operation result to the central computing component, wherein the initial operation result is encrypted based on a third preset key pair before being sent.

[0106] The central computing component 330 is used to decrypt the initial operation result based on a third preset key pair, and to perform a second operation on the initial operation result based on preset processing logic to obtain a second operation result; and to decrypt the second operation result based on a preset Paillier decryption key pair to obtain the target operation data.

[0107] The data encryption transmission device provided by this invention avoids the leakage of real data during transmission by adding noise to the transmitted data. At the same time, the security of the data transmission process is further improved by using a key pair composed of a public key and a private key to perform sequential encryption and decryption processes on adjacent components of the data transmission, achieving the purpose of preventing eavesdropping. On this basis, the Paillier encryption measure is superimposed, which further enhances the security of the entire data transmission process, realizing multiple confidentiality processing of the data encryption transmission process.

[0108] According to the data encryption transmission device provided by the present invention, the device 300 further includes: a key center component 340, which is used to generate a Paillier encryption key and a Paillier decryption key, and send the Paillier encryption key to the data acquisition component and the fog computing component respectively, and send the Paillier decryption key to the central computing component.

[0109] The key center component is also used to: obtain the optimal noise x corresponding to the random perturbation value based on the Laplace parameter (μ,b). i The user data is then subjected to noise enhancement based on the optimal noise level to obtain noisy user data.

[0110] It should be noted that this key center component enables data to be encrypted using the Paillier encryption key in both the data acquisition and fog computing components, and decrypted using the Paillier decryption key in the central computing component. This improves the security and confidentiality of data transmission. At the same time, the key center component can also be used to find the optimal noise value for the transmitted data and add noise to it, so that the transmitted data can be encrypted to the maximum extent while ensuring the accuracy of the transmitted information.

[0111] According to the data encryption transmission device provided by the present invention, in the present invention, the data acquisition component 310 is further used to generate a first preset key pair, the fog computing component 320 is further used to generate a second preset key pair, and the central computing component 330 is further used to generate a third preset key pair.

[0112] It should be noted that, in order to avoid the key center component generating and transmitting key pairs for each party, the data acquisition component, fog computing component, and central computing component each generate their own keys, thus preventing security risks caused by attacks on the key center and eavesdropping on key transmissions.

[0113] Figure 4 This is a schematic diagram of the data encryption transmission system provided by the present invention, as shown below. Figure 4 As shown, this data encryption and transmission system includes: a cryptographic component module, a privacy-utility trade-off module, and a data management module. The cryptographic component module provides the underlying cryptographic algorithm package, supporting the implementation of the Paillier algorithm and RSA algorithm. The privacy-utility trade-off module is the foundation for data noise reduction. The key center calculates the information entropy and signal-to-noise ratio based on the collected perturbed smart meter data, and derives the final total utility function after determining their weights. The Laplace parameter value is obtained under the constraints of minimum information entropy, minimum signal-to-noise ratio, and convergence of the total utility function. The data management module provides algorithms for data aggregation and hashing. Data aggregation is used to summarize the electricity consumption values ​​of smart meters while avoiding the leakage of individual user's true value. Hashing provides a security guarantee for data storage on the blockchain. The interaction between modules is as follows: the key generated by the cryptographic component module is used for data encryption / decryption and data transmission security guarantees in the privacy-utility trade-off module and the data management module; the Laplace parameter obtained by the privacy-utility trade-off module is used for data noise reduction during the data aggregation stage in the data management module, and finally, a secure on-chain value is obtained based on hash calculation, completing data storage.

[0114] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a data encryption transmission method, which includes: acquiring a random perturbation value sent by a fog computing component, wherein the random perturbation value is encrypted based on a first preset key pair before being sent; decrypting the random perturbation value based on the first preset key pair, and performing perturbation processing based on the random perturbation value to obtain perturbation data; adding noise to user data based on the perturbation data to obtain noisy user data; and sending the noisy user data to the fog computing component, wherein the noisy user data is encrypted based on a preset Paillier encryption key and a second preset key pair before being sent.

[0115] The noisy user data is decrypted based on the second preset key pair, and an initial operation is performed on the noisy user data based on the preset processing logic to obtain the initial operation result; the initial operation result is sent to the central computing component, and the initial operation result is encrypted based on the third preset key pair before being sent.

[0116] Based on the third preset key pair, the initial operation result is decrypted, and the initial operation result is then processed again based on preset processing logic to obtain the secondary operation result; based on the preset Paillier decryption key pair, the secondary operation result is decrypted to obtain the target operation data.

[0117] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the data encryption transmission method provided by the above methods, the method comprising: acquiring a random perturbation value sent by a fog computing component, wherein the random perturbation value is encrypted based on a first preset key pair before being sent; performing perturbation processing on the random perturbation value based on the first preset key pair to obtain perturbation data; performing decryption processing on the perturbation data; and performing noise addition processing on user data based on the random perturbation value to obtain noisy user data; and sending the noisy user data to the fog computing component, wherein the noisy user data is encrypted based on a preset Paillier encryption key and a second preset key pair before being sent;

[0119] The noisy user data is decrypted based on the second preset key pair, and an initial operation is performed on the noisy user data based on the preset processing logic to obtain the initial operation result; the initial operation result is sent to the central computing component, and the initial operation result is encrypted based on the third preset key pair before being sent.

[0120] Based on the third preset key pair, the initial operation result is decrypted, and the initial operation result is then processed again based on preset processing logic to obtain the secondary operation result; based on the preset Paillier decryption key pair, the secondary operation result is decrypted to obtain the target operation data.

[0121] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the data encryption transmission methods provided above. The method includes: acquiring a random perturbation value sent by a fog computing component, wherein the random perturbation value is encrypted based on a first preset key pair before being sent; decrypting the random perturbation value based on the first preset key pair, and performing perturbation processing based on the random perturbation value to obtain perturbation data; adding noise to user data based on the perturbation data to obtain noisy user data; and sending the noisy user data to the fog computing component, wherein the noisy user data is encrypted based on a preset Paillier encryption key and a second preset key pair before being sent.

[0122] The noisy user data is decrypted based on the second preset key pair, and an initial operation is performed on the noisy user data based on the preset processing logic to obtain the initial operation result; the initial operation result is sent to the central computing component, and the initial operation result is encrypted based on the third preset key pair before being sent.

[0123] Based on the third preset key pair, the initial operation result is decrypted, and the initial operation result is then processed again based on preset processing logic to obtain the secondary operation result; based on the preset Paillier decryption key pair, the secondary operation result is decrypted to obtain the target operation data.

[0124] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0125] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for encrypted data transmission, characterized in that, include: Obtain a random perturbation value sent by the fog computing component, wherein the random perturbation value is encrypted based on a first preset key pair before being sent; The random perturbation value is decrypted based on the first preset key pair, and perturbation processing is performed based on the random perturbation value to obtain perturbation data. The user data is then noise-added based on the perturbation data to obtain noisy user data. The noisy user data is sent to the fog computing component. Before the noisy user data is sent, it is encrypted based on a preset Paillier encryption key and a second preset key pair. The noisy user data is decrypted based on the second preset key pair, and the noisy user data is subjected to an initial operation based on the preset processing logic to obtain the initial operation result. The initial calculation result is sent to the central computing component. Before the initial calculation result is sent, it is encrypted based on a third preset key pair. Based on the third preset key pair, the initial operation result is decrypted, and the initial operation result is then processed again based on preset processing logic to obtain the secondary operation result; based on the preset Paillier decryption key pair, the secondary operation result is decrypted to obtain the target operation data.

2. The data encryption transmission method according to claim 1, characterized in that, The step of adding noise to user data based on the perturbation data to obtain noisy user data specifically includes: Obtaining the optimal noise Obeying position parameters and degree parameters of distributed ; based on parameter Obtain the optimal noise corresponding to the random perturbation value The user data is then subjected to noise enhancement based on the optimal noise level to obtain noisy user data.

3. The data encryption transmission method according to claim 2, characterized in that, The acquisition of optimal noise Obeying position parameters and degree parameters of distributed Specifically, it includes: Define the total utility function ,in, Represents information entropy. Indicates the signal-to-noise ratio. Represents a non-negative number less than 1; make , And the total utility function converges, thus obtaining distributed ,in, This indicates the preset minimum information entropy. This indicates the preset minimum signal-to-noise ratio.

4. The data encryption transmission method according to claim 3, characterized in that, The signal-to-noise ratio formula is: The formula for information entropy is: in, Represents the variance of the signal. The variance of the noise is represented. This represents the variance of the Laplace distribution. This represents the probability density function of the signal.

5. The data encryption transmission method according to claim 2, characterized in that, The method further includes: each of the preset key pairs includes a public key and a private key; the public key of the first preset key pair, the second preset key pair, and the third preset key pair, the random perturbation value, the initial operation result, the target operation data, and the... parameter Stored in a blockchain ledger.

6. A data encryption transmission device, characterized in that, For performing the method according to any one of claims 1-5, comprising: A data acquisition component is used to acquire random perturbation values ​​sent by a fog computing component. These random perturbation values ​​are encrypted using a first preset key pair before being sent. The random perturbation values ​​are then decrypted using the first preset key pair, and perturbation processing is performed on them to obtain perturbation data. Noise is added to user data based on the perturbation data to obtain noisy user data. The noisy user data is then sent to the fog computing component, and the noisy user data is encrypted using a preset Paillier encryption key and a second preset key pair before being sent. The fog computing component is used to decrypt the noisy user data based on a second preset key pair, and perform an initial operation on the noisy user data based on a preset processing logic to obtain an initial operation result; the initial operation result is sent to the central computing component, and the initial operation result is encrypted based on a third preset key pair before being sent. The central computing component is used to decrypt the initial calculation result based on a third preset key pair, and to perform a second calculation on the initial calculation result based on preset processing logic to obtain a second calculation result; and to decrypt the second calculation result based on a preset Paillier decryption key pair to obtain the target calculation data.

7. The data encryption transmission device according to claim 6, characterized in that, The device further includes a key center component, used to generate a Paillier encryption key and a Paillier decryption key, and send the Paillier encryption key to the data acquisition component and the fog computing component respectively, and send the Paillier decryption key to the central computing component.

8. The data encryption transmission device according to claim 7, characterized in that, The key center component is also used for: based on parameter Obtain the optimal noise corresponding to the random perturbation value The user data is then subjected to noise enhancement based on the optimal noise level to obtain noisy user data.

9. The data encryption transmission device according to claim 6, characterized in that, The data acquisition component is also used to generate a first preset key pair, the fog computing component is also used to generate a second preset key pair, and the central computing component is also used to generate a third preset key pair.

10. A blockchain-based encrypted data transmission system, characterized in that, This includes a blockchain network and a data encryption transmission device as described in any one of claims 6-9.

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