Smart grid fine-grained data secure transmission method and related equipment

Abstract

The present application provides a method for securely transmitting fine-grained data of a smart grid and related equipment. The method includes acquiring multiple data, processing the multiple data based on predetermined parameters to obtain processed data; performing base conversion on the numerical values of integer digits in the processed data to obtain first converted data; randomly generating a probability corresponding to the first converted data, and based on the probability, perturbing the first converted data according to predetermined rules to obtain second converted data; merging the second converted data and the numerical values of decimal digits in the processed data to obtain target data; and sending the target data to a data receiving end. This method solves the technical problem in the prior art that privacy processing of data affects the accuracy of data, and achieves the purpose of ensuring the accuracy and security of transmitted data.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method for securely transmitting fine-grained data of a smart grid and related equipment. Background Art

[0002] In large-scale electricity consumption scenarios, traditional power distribution models are no longer able to meet the growing demand. To address this issue, intelligent power grid systems have received widespread attention and are rapidly developing. Within these systems, smart power data centers are the core hub of the grid, responsible for data analysis and computation, as well as real-time power regulation. To ensure a consistent supply and demand, smart power data centers must collect granular electricity usage data from user-side smart meters. However, directly submitting raw electricity usage data carries the risk of leaking personal privacy, potentially revealing critical private information such as user behavior patterns and lifestyle habits.

[0003] To ensure user privacy, a privacy protection mechanism must be implemented throughout the data transmission process to process electricity usage data. However, after the data is processed through the privacy protection mechanism, the accuracy of the aggregated data is low, and the aggregated data is unusable, making it difficult to effectively resolve the conflict between privacy protection and data accuracy. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a secure transmission method and related equipment for fine-grained data of a smart grid to overcome all or part of the deficiencies in the prior art.

[0005] Based on the above-mentioned purpose, the present application provides a secure transmission method for fine-grained data of a smart grid, which is applied to a data sending end, including: obtaining multiple data, processing the multiple data based on predetermined parameters to obtain processed data; performing base conversion on the numerical values of integer digits in the processed data to obtain first converted data; randomly generating a probability corresponding to the first converted data, and based on the probability, perturbing the first converted data according to predetermined rules to obtain second converted data; merging the second converted data and the numerical values of decimal places in the processed data to obtain target data; and sending the target data to a data receiving end.

[0006] Optionally, the processing of the multiple data based on predetermined parameters to obtain processed data includes: performing noise processing on the multiple data based on predetermined parameters; and integrating the multiple data after the noise processing to obtain the processed data.

[0007] Optionally, the noise processing is performed on the multiple data based on predetermined parameters, including: generating a sequence corresponding to the multiple data based on the predetermined parameters; for each data in the multiple data, randomly extracting sequence data from the sequence, and adding the sequence data to obtain initial noise corresponding to the data; multiplying the initial noise by the predetermined parameters to obtain amplified noise corresponding to the data, and adding the amplified noise to the data.

[0008] Optionally, generating the sequences corresponding to the multiple data based on the predetermined parameters includes: generating two initial sequences that conform to the gamma distribution based on the predetermined parameters; and subtracting the two initial sequences to obtain the sequences corresponding to the multiple data.

[0009] Optionally, before performing base conversion on integer bits in the processed data to obtain first converted data, the method includes: adding the processed data to a predetermined value.

[0010] Optionally, based on the probability, the first conversion data is disturbed according to a predetermined rule to obtain the second conversion data, including: in response to determining that the probability is less than a predetermined probability, inverting the character corresponding to the predetermined data bit in the first conversion data to obtain the second conversion data.

[0011] The present application also provides a method for secure transmission of fine-grained data of a smart grid, which is applied to a data receiving end, and includes: receiving target data sent by a data sending end; performing base conversion on the numerical value of the integer digits of the target data to obtain third converted data; performing frequency statistics on each character of a predetermined digit in the third converted data to obtain a statistical result, and determining a deviation of the third converted data based on the statistical result; and based on the deviation, adding the total sum of the characters in the third converted data to the deviation to perform deviation correction to obtain final data.

[0012] Optionally, determining the deviation of the third conversion data based on the statistical result includes:

[0013] ,in, For the deviation, is the total number of data in the third converted data, is a pre-set probability, is the statistical number of 1 in the statistical result, is the predetermined data bit weight.

[0014] Based on the same inventive concept, the present application also provides a secure transmission device for fine-grained data of a smart grid, which is applied to a data sending end and includes: a processing module, configured to obtain multiple data, and process the multiple data based on predetermined parameters to obtain processed data; a first conversion module, configured to perform base conversion on the numerical value of the integer digits in the processed data to obtain first converted data; a perturbation module, configured to randomly generate a probability corresponding to the first converted data, and based on the probability, perturb the first converted data according to a predetermined rule to obtain second converted data; a first merging module, configured to merge the second converted data and the numerical value of the decimal digits in the processed data to obtain target data; and a sending module, configured to send the target data to a data receiving end.

[0015] Based on the same inventive concept, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method described above when executing the computer program.

[0016] From the above description, it can be seen that the secure transmission method and related equipment of fine-grained data of the smart grid provided by the present application include acquiring multiple data, processing the multiple data based on predetermined parameters to obtain processed data, and combining scattered data into a whole, thereby eliminating the fragmentation of multiple data. Performing base conversion on the numerical value of the integer digits in the processed data to obtain first conversion data, which facilitates privacy encryption processing of the processed data. Randomly generating the probability corresponding to the first conversion data, and based on the probability, perturbing the first conversion data according to predetermined rules to obtain second conversion data, further ensuring the security of the processed data. Combining the second conversion data and the numerical value of the decimal place in the processed data to obtain target data, so that the determined target data is secure. Sending the target data to the data receiving end ensures the security of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart of a method for securely transmitting fine-grained data of a smart grid according to an embodiment of the present application is provided;

[0019] Figure 2 A schematic diagram of a data publishing model constructed in an embodiment of the present application;

[0020] Figure 3 A schematic diagram of another data publishing model constructed in an embodiment of the present application;

[0021] Figure 4 This is a flow chart of a method for securely transmitting fine-grained data of a smart grid according to another embodiment of the present application;

[0022] Figure 5 This is a schematic diagram of the structure of a secure transmission device for fine-grained data of a smart grid according to an embodiment of the present application;

[0023] Figure 6 This is a schematic structural diagram of a secure transmission device for fine-grained data of a smart grid according to another embodiment of the present application;

[0024] Figure 7 This is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0026] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0027] As described in the background technology section, in large-scale electricity consumption scenarios, the traditional power distribution model can no longer adapt to the growing demand for electricity. In traditional power distribution networks, there are problems such as poor power regulation capabilities, slow information flow, and high operating costs. In order to solve this problem, intelligent power grid systems have received widespread attention and are in a state of rapid development. In an intelligent power grid system, the intelligent power data center is the core hub of the power grid, responsible for data analysis and calculation and real-time power regulation. To ensure the matching of power supply and demand, the intelligent power data center needs to collect fine-grained power consumption data provided by smart meters at the user end. Directly submitting raw power consumption data poses the risk of leaking personal privacy. The above-mentioned power consumption data may be analyzed by data analysis methods such as machine learning and data mining, so that key privacy information such as user behavior patterns and living habits can be obtained.

[0028] To ensure user privacy, a privacy-preserving mechanism is required to process electricity usage data. However, after electricity usage data is processed by the privacy-preserving mechanism, the data aggregation results obtained by the smart power data center are inaccurate and unusable. When using existing privacy-preserving models for data privacy protection, these models struggle to effectively resolve the conflict between privacy protection and data accuracy. This privacy-preserving model can be a distributed differential privacy system model, which adds noise to the data before uploading it to the data center. This model ensures that the data center can obtain the aggregated data without obtaining the specific electricity data values for each user.

[0029] Furthermore, existing literature has explored data encryption technologies such as homomorphic encryption. However, these privacy-preserving schemes consume smart meter computing resources. Because smart meters provide real-time, fine-grained electricity usage data, they must submit data to the data center multiple times throughout the day. Existing literature has explored solutions using rechargeable batteries, but this requires users to purchase them, increasing costs and posing fire safety risks. Smart meters require encrypting large amounts of data, significantly consuming their computing resources. Furthermore, existing literature has explored solutions using differential privacy, but these solutions fail to consider the balance between data availability and privacy. Some solutions also disrupt the temporal correlation of data during data processing, preventing the data center from gaining detailed information about electricity demand at a specific point in time for users within the same block.

[0030] In view of this, the present application embodiment proposes a method for secure transmission of fine-grained data of a smart grid, referring to Figure 1 , applied to the data sending end, including the following steps:

[0031] Step 101: Acquire multiple data, and process the multiple data based on predetermined parameters to obtain processed data.

[0032] In this step, multiple data are acquired. The data may be time-correlated power data from a smart grid. A smart grid refers to an intelligent power grid system based on Internet of Things technology, primarily comprising a communications network and a distribution network. Data analysis is performed in a data center to achieve intelligent scheduling of power resources. Directly transmitting multiple data sets, which are irregular and relatively large in number, will cause data reception pressure at the data receiving end. Therefore, the multiple data sets need to be processed using predetermined parameters to obtain processed data. The processed data can reflect the multiple data sets, integrating the scattered data into a whole and eliminating the fragmented nature of the multiple data sets.

[0033] It should be noted that the data obtained can come from predetermined users. Assuming there are n users in the block, the user set is The data set is defined as A, and the data of each user collected at time t is defined as , equal All electricity consumption data of the i-th user in one day is expressed as , equal For example, the electric meter in the smart grid will publish the current power consumption every 15 minutes. It means the power consumption of the i-th user in the user set at time t, in kw. The total power consumption of the block at time t is expressed as The unit is kw. The data can reflect the source of the data and the correlation between the data and time.

[0034] Step 102: performing base conversion on the integer digits in the processed data to obtain first converted data.

[0035] In this step, the processed data includes integer and decimal values. To ensure the subsequent transmission security of the processed data and the accuracy of the processed data obtained by the data receiving end, the processed data needs to be encrypted. Therefore, the integer values in the processed data are converted to binary values, and the decimal values are converted to binary values to obtain the first converted data. Binary values are represented by multiple 0s and 1s. By changing the binary values, the privacy encryption of the processed data is facilitated.

[0036] Step 103: randomly generate a probability corresponding to the first conversion data, and based on the probability, perturb the first conversion data according to a predetermined rule to obtain second conversion data.

[0037] In this step, the probability corresponding to the first conversion data is randomly generated. For example, a random number generator ranging from 0 to 1 can be used to generate the probability corresponding to the first conversion data. Through the probability, it can be determined that the first conversion data is disturbed according to a predetermined rule to obtain the second conversion data. The original processed data is further modified so that the subsequent data receiving end obtains the accuracy of the processed data, further ensuring the security of the processed data. It should be noted that after the first conversion data is disturbed according to the predetermined rule, the disturbed first conversion data is converted from a binary value to a decimal value.

[0038] Step 104: Combine the decimal places in the second converted data and the processed data to obtain target data.

[0039] In this step, the decimal values in the second conversion data and the processed data are merged to obtain the target data. The integer values in the target data are changed, and the target data is not exactly the same as the aforementioned processed data, so that the determined target data is secure.

[0040] Step 105: Send the target data to a data receiving end.

[0041] In this step, the target data is sent to a data receiving end, which can be a server. Multiple data are processed using various processing techniques to obtain the target data, ensuring the accuracy of subsequent data decryption by the data receiving end and the security of data transmission.

[0042] It should be noted that in the smart grid scenario, all users in the grid publish and aggregate data layer by layer through the constructed data publishing model. The model is mainly divided into user layer, aggregation layer and center, such as Figure 2 As shown. The user layer is responsible for data collection, monitoring and processing. The aggregation layer is responsible for aggregating data within the same block. In the aggregation layer, each block is managed by a data aggregator. The blocks are divided by the user's geographic location. The aggregator in each block is responsible for collecting and aggregating data. The center is located at the top level of the model. By analyzing the data sent by the aggregation layer, it formulates and publishes appropriate power dispatch measures. Strict privacy protection principles are implemented in the above model, and all data nodes except the user themselves are considered untrustworthy. All users in the above model will independently complete the differential privacy processing of data on their own smart meters, and use the separability of the Laplace distribution proposed above to generate independent Laplace noise. Each user will perturb the data with a certain probability, further disrupting the data and destroying the data pattern. Finally, it is published to the aggregator, and the smart power data center performs data analysis, as shown Figure 3 shown.

[0043] Through the above scheme, multiple data are acquired, and the multiple data are processed based on predetermined parameters to obtain processed data, thereby combining the scattered data into a whole and eliminating the fragmentation of the multiple data. The integer digits in the processed data are converted into a base to obtain first converted data, which facilitates privacy encryption of the processed data. The probability corresponding to the first converted data is randomly generated. Based on the probability, the first converted data is perturbed according to predetermined rules to obtain second converted data, further ensuring the security of the processed data. The second converted data is combined with the decimal digits in the processed data to obtain target data, ensuring the security of the determined target data. The target data is sent to the data receiving end, ensuring the security of data transmission.

[0044] In some embodiments, the processing of the multiple data based on predetermined parameters to obtain processed data includes: performing noise processing on the multiple data based on predetermined parameters; and integrating the multiple data after the noise processing to obtain the processed data.

[0045] In this embodiment, multiple data sets are subjected to noise processing based on predetermined parameters, such that the noised data differs from the original data, thereby ensuring the privacy of the multiple data sets and making the noised data less susceptible to decryption. It should be noted that the predetermined parameter may be a predetermined scale parameter determined by the ratio of a predetermined data sensitivity to a predetermined privacy budget. The multiple noised data sets are then summed to integrate the noised data sets, generating processed data. This data integration is then performed at the data sending end, where the multiple data sets are processed, thus avoiding the subsequent transmission of fragmented data to the data receiving end.

[0046] It should be noted that the noise can be N-dimensional noise. The significance of generating N-dimensional noise here is to use this N-dimensional discrete data to represent the result of subtracting two continuous gamma distributions, which is equivalent to sampling these two continuous gamma distributions. At time t, the designed distributed Laplace mechanism can be expressed as ,in, yes The search function. is a collection of n user noises, is the sum of N-dimensional noise data, are two continuous gamma distributions Discretized sampling of a distribution.

[0047] In some embodiments, the noise processing is performed on the multiple data based on predetermined parameters, including: generating a sequence corresponding to the multiple data based on the predetermined parameters; for each data in the multiple data, randomly extracting sequence data from the sequence, and adding the sequence data to obtain initial noise corresponding to the data; multiplying the initial noise by the predetermined parameters to obtain amplified noise corresponding to the data, and adding the amplified noise to the data.

[0048] In this embodiment, a sequence corresponding to multiple data items is generated based on predetermined parameters, converting the multiple data items into an ordered sequence, thereby maintaining order. Noise is added to each data item. To ensure the randomness of the noise, sequence data is randomly extracted from the sequence and each character in the sequence data is summed to obtain the initial noise corresponding to the data item. The initial noise is multiplied by the predetermined parameter to obtain the amplified noise corresponding to the data item. This amplified noise makes the noise item more prominent. Adding the amplified noise to the data enhances its security.

[0049] In some embodiments, generating the sequences corresponding to the plurality of data based on the predetermined parameters includes: generating two initial sequences conforming to a gamma distribution based on the predetermined parameters; and subtracting the two initial sequences to obtain the sequences corresponding to the plurality of data.

[0050] In this embodiment, two initial sequences conforming to the gamma distribution are generated based on predetermined parameters. , , and subtracting the two distributions yields The distribution of gamma distribution makes multiple data orderly. It should be noted that there are two parameters that determine the gamma distribution, one is the shape parameter and the other is the scale parameter. Assuming that we want to get data of gamma (shape parameter a, scale parameter b), we can use gamma (shape parameter a, scale parameter 1) to generate data, and then multiply the data by b to get data of gamma (a, b). It should be noted that, taking time t as an example, first, the smart meters in the block will be based on the privacy budget agreed with the center. and data sensitivity , a discrete N-dimensional noise data is generated locally using two subtracted gamma distributions.

[0051] It's also worth noting that the gamma distribution utilizes a modified Laplace mechanism. In this Laplace mechanism, when adding Laplace-distributed noise, to ensure the accuracy of the aggregated result (i.e., the noise sum is zero), a Laplace probability density function with a zero location parameter is required. Based on this characteristic and several properties related to the Laplace distribution, the Laplace distribution can be decomposed into several gamma distributions. Before proceeding with the derivation, let's first list the three main properties used in the derivation.

[0052] Property 1: Laplace distribution is a symmetric distribution. When the location parameter is 0, the probability density curve of the Laplace distribution is symmetric about the y-axis. At this time, the absolute value of the probability density function is eliminated, and two symmetric exponential distributions can be combined into a Laplace distribution. The probability density function is .

[0053] Property 2: Gamma distribution has exponential characteristics. If the random variable Y obeys the gamma distribution, that is .when When , Y follows an exponential distribution.

[0054] Property 3: Additivity of the gamma distribution, with n random variables ,So .

[0055] The derivation process is as follows:

[0056] Suppose there are n random variables , n random variables .

[0057] From Property 3, we know that , .

[0058] From Property 2, we can get , Obeys exponential distribution.

[0059] From Property 1, we can see that .

[0060] therefore, .

[0061] Therefore, the Laplace distribution can be composed of several gamma distributions subtracted from each other.

[0062] In some embodiments, before performing base conversion on integer bits in the processed data to obtain first converted data, the method includes: adding the processed data to a predetermined value.

[0063] In this embodiment, the processed data is added to a predetermined value. On the one hand, this is to ensure that the disturbance value of each data is a positive number before the data is disturbed; on the other hand, adding a larger fixed value to the data can ensure that the overall data is greater than the disturbance value, thereby achieving sufficient disturbance of the data.

[0064] In some embodiments, based on the probability, the first conversion data is perturbed according to a predetermined rule to obtain the second conversion data, including: in response to determining that the probability is less than the predetermined probability, the character corresponding to the predetermined data bit in the first conversion data is inverted to obtain the second conversion data.

[0065] In this embodiment, when the probability is less than a predetermined probability, the character corresponding to the predetermined data in the first converted data needs to be inverted. For example, the inversion operation is performed on bits x through a+x-1 (a bits in total) in an 8-bit binary sequence, ensuring sufficient perturbation of the first converted data. The bit perturbation algorithm divides the input data into an integer part and a decimal part. The integer part is then binary-encoded, converting it into 8-bit binary data. Selected data bits are then perturbed, inverting the data bits with a probability of p, thereby changing the data size. The perturbed integer and decimal parts are then added together to obtain the final perturbed data, ensuring that the first converted data is fully perturbed.

[0066] In the bit perturbation algorithm, multiple data bits and different probabilities can be perturbed, further disrupting the data pattern within a continuous time period. The model protects user privacy while ensuring the availability of aggregated results. The bit perturbation algorithm is defined as disturbance, and value is a fixed constant. The final published data can be expressed as disturbance[ ]. The data at each moment t All through disturbance[ ] processing. In the bit perturbation algorithm, multiple data bits and different probabilities can be perturbed, further disrupting the data patterns within a continuous time period. The model ensures the usability of aggregated results while protecting user privacy.

[0067] In view of this, the present application embodiment proposes a method for secure transmission of fine-grained data of a smart grid, referring to Figure 4 , applied to the data receiving end, including the following steps:

[0068] Step 201: Receive target data sent by a data sending end.

[0069] In this step, target data sent by a data sending end is received, wherein the data sending end may be a smart grid.

[0070] Step 202: Perform base conversion on the integer digits of the target data to obtain third converted data.

[0071] In this step, since the integer bit values of the target data are obtained through conversion, in order to restore the target data to the processed data, it is necessary to perform base conversion on the integer bit values of the target data to obtain the third converted data, which lays the foundation for restoring the target data to the processed data.

[0072] Step 203 : Perform frequency statistics on each character at a predetermined position in the third converted data to obtain a statistical result, and determine the deviation of the third converted data based on the statistical result.

[0073] In this step, a frequency count is performed for each character corresponding to a predetermined position in the third converted data to obtain a statistical result. The predetermined position is a predetermined data position. The statistical result can be used to determine the deviation of the third converted data. The deviation can be used to determine the data position that has been changed in the third converted data, thereby achieving the purpose of understanding the transformation of the third converted data.

[0074] It should be noted that relative error mainly measures the distance between two data. The larger the error, the greater the distance between the two data and the greater the difference. Assume that the original data is , the processed data is The relative error of the data is defined as: The entropy increase rate is mainly used to measure the change of a sequence. When the sequence is more disordered, the entropy increase rate becomes larger. The calculation formula of discrete information entropy is ,in For data series elements, P represents the probability. Assume that the information entropy of the original data sequence is , the information entropy of the processed data sequence is , then the entropy increase rate is: In the model, relative error is used to measure data accuracy, and information entropy is used to measure data privacy. When the two are equal, data accuracy and privacy are considered equivalent, and the privacy budget is balanced.

[0075] Step 204 : Based on the deviation, the character sum of the third converted data is added to the deviation to perform deviation correction to obtain final data.

[0076] In this step, based on the deviation, the third conversion data can be corrected, and the deviation is added to the sum of the characters of the third conversion data to correct the deviation and obtain the final data. Data privacy protection also needs to be kept confidential on the receiving end. The data receiving end can only know the sum of all data. Through the sum, the total power consumption in the current time zone can be known. By continuously obtaining the total power consumption of a certain area at different times, the power resources can be scheduled and allocated according to the power consumption. Therefore, the recovered deviation is a total deviation, and each specific data cannot be recovered. In summary, the calculated deviation is a deviation of the sum of the characters of the third conversion data. This ensures the accuracy of the data received by the receiving end.

[0077] Through the above scheme, target data sent by a data transmitter is received. The integer digits of the target data are converted to base 1, resulting in third converted data. This provides the basis for restoring the target data to processed data. Frequency statistics are performed on each character in the third converted data to obtain a statistical result. Based on the statistical result, a deviation of the third converted data is determined, thereby determining the transformations that the third converted data has undergone. Based on the deviation, the sum of the characters in the third converted data is added to the deviation for deviation correction to obtain final data, thereby ensuring the accuracy of the data subsequently received by the data receiver.

[0078] In some embodiments, determining the deviation of the third conversion data based on the statistical result includes: ,in, For the deviation, is the total number of data in the third converted data, is a pre-set probability, is the statistical number of 1 in the statistical result, is the predetermined data bit weight.

[0079] In this embodiment, the deviation can be derived based on the following ideas: The random response mechanism is a typical distributed mechanism. Suppose there are m people, each of whom has a small ball, and the colors of the balls are red and green. Among them, there are b people with green balls. The color of the balls owned by these m people is asked in turn, and each person responds with The probability of answering a color different from the color of the ball in hand. After statistics, we know that Individuals have green balls, and the rest According to the statistical results, the maximum likelihood estimation method can be used to know the real number of green balls. In the bit perturbation algorithm, the two colors of the balls are mapped to 0 and 1 on the data bit. The probability of converting 0 to 1 and 0 to 1 is used to achieve data perturbation. Then, based on the statistical 0:1 ratio, the real 0:1 ratio is estimated. The difference is that in the bit perturbation algorithm, the data error needs to be calculated based on the estimated 0:1 ratio and the weight w of the data bit. Assume that there are m samples in total, and the ratio of 1 in m samples is , the number of 1s after counting is , the weight of the data bit is w, and the data deviation error is calculated.

[0080] The probability of 1 after statistics can be expressed as: .

[0081] The probability of 0 after statistics can be expressed as: .

[0082] Establish the likelihood function: .

[0083] Take the logarithm: .

[0084] make , The maximum likelihood estimate of , hour: .

[0085] So the number of 1s before the disturbance is , the number of 0 is .

[0086] Therefore, the deviation can be calculated: .

[0087] The design of the bit perturbation algorithm is based on the above theoretical analysis. What needs to be pointed out is that the algorithm and privacy budgets The relationship is Generally speaking, the privacy budget is usually between 0 and 1, and privacy is the value of the privacy budget. The value of is usually between 0 and 0.5. When it is greater than 0.5, the calculated privacy budget is negative and The values from 0 to 0.5 show an odd symmetric relationship. The range is [0,0.5).

[0088] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0089] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a secure transmission device for fine-grained data of a smart grid.

[0091] refer to Figure 5 , applied to a data sending end, the secure transmission device for fine-grained data of a smart grid comprises:

[0092] The processing module 10 is configured to acquire a plurality of data and process the plurality of data based on predetermined parameters to obtain processed data;

[0093] A first conversion module 20 is configured to perform base conversion on the integer digits in the processed data to obtain first converted data;

[0094] The perturbation module 30 is configured to randomly generate a probability corresponding to the first conversion data, and based on the probability, perturb the first conversion data according to a predetermined rule to obtain second conversion data;

[0095] A first merging module 40 is configured to merge the second converted data and the decimal values in the processed data to obtain target data;

[0096] The sending module 50 is configured to send the target data to a data receiving end.

[0097] The above-mentioned device acquires multiple data, processes the multiple data based on predetermined parameters, and obtains processed data, thereby combining the scattered data into a whole and eliminating the fragmentation of the multiple data. The integer digits in the processed data are converted to a base to obtain first converted data, which facilitates privacy encryption of the processed data. Probabilities corresponding to the first converted data are randomly generated. Based on the probabilities, the first converted data is perturbed according to predetermined rules to obtain second converted data, further ensuring the security of the processed data. The second converted data is combined with the decimal digits in the processed data to obtain target data, ensuring the security of the determined target data. The target data is sent to a data receiving end, ensuring the security of data transmission.

[0098] In some embodiments, the processing module 10 is further configured to perform noise processing on the multiple data based on predetermined parameters; and integrate the multiple data after the noise processing to obtain the processed data.

[0099] In some embodiments, the processing module 10 is further configured to generate a sequence corresponding to the multiple data based on the predetermined parameters; for each data among the multiple data, randomly extract sequence data from the sequence, and add the sequence data to obtain initial noise corresponding to the data; multiply the initial noise by the predetermined parameters to obtain amplified noise corresponding to the data, and add the amplified noise to the data.

[0100] In some embodiments, the processing module 10 is further configured to generate two initial sequences conforming to the gamma distribution based on the predetermined parameters; and subtract the two initial sequences to obtain sequences corresponding to the plurality of data.

[0101] In some embodiments, an adding module is further included, and the adding module is configured to add the processed data to a predetermined value before performing base conversion on the integer bits in the processed data to obtain the first converted data.

[0102] In some embodiments, the disturbance module 30 is further configured to, in response to determining that the probability is less than a predetermined probability, invert a character corresponding to a predetermined data bit in the first converted data to obtain the second converted data.

[0103] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a secure transmission device for fine-grained data of a smart grid.

[0104] refer to Figure 6 The secure transmission device for fine-grained data of the smart grid is applied to the data sending end and includes:

[0105] The receiving module 60 is configured to receive target data sent by the data sending end;

[0106] The second conversion module 70 is configured to perform base conversion on the integer digits of the target data to obtain third converted data;

[0107] a determination module 80 configured to perform frequency statistics on each character at a predetermined position in the third conversion data to obtain a statistical result, and determine a deviation of the third conversion data based on the statistical result;

[0108] The correction module 90 is configured to add the character sum of the third converted data to the deviation based on the deviation, perform deviation correction, and obtain final data.

[0109] The target data sent by the data transmitting end is received by the above-mentioned device. The integer digits of the target data are converted into base 1, thereby obtaining third converted data, which lays the foundation for restoring the target data into processing data. The frequency of each character in the third converted data is counted to obtain a statistical result. Based on the statistical result, the deviation of the third converted data is determined, thereby achieving the purpose of knowing the transformation undergone by the third converted data. Based on the deviation, the third converted data is corrected to obtain fourth converted data, thereby ensuring the accuracy of the fourth converted data. The fourth converted data is combined with the decimal digits of the target data to obtain the final data, thereby ensuring the accuracy of the subsequent decryption of the data by the data receiving end.

[0110] In some embodiments, the determining module 80 is further configured to ,in, For the deviation, is the total number of data in the third converted data, is a pre-set probability, is the statistical number of 1 in the statistical result, is the predetermined data bit weight.

[0111] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0112] The apparatus of the above embodiment is used to implement the corresponding method for secure transmission of fine-grained data of the smart grid in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0113] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the secure transmission method of smart grid fine-grained data as described in any of the above embodiments.

[0114] Figure 7 A more specific hardware structure diagram of an electronic device provided in this embodiment is shown. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0115] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0116] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0117] The input / output interface 1030 is used to connect to an input / output module to enable information input and output. The input / output module can be configured as a component within the device (not shown) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc. Output devices may include a display, speaker, vibrator, indicator light, etc.

[0118] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.).

[0119] The bus 1050 comprises a path for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0120] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0121] The electronic device of the above embodiment is used to implement the corresponding method for secure transmission of fine-grained data of the smart grid in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0122] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the secure transmission method of smart grid fine-grained data as described in any of the above embodiments.

[0123] The computer-readable media of this embodiment includes permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0124] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for securely transmitting fine-grained data of the smart grid as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0125] Based on the same concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a computer program product, including computer program instructions. When the computer program instructions are run on a computer, the computer executes the method for secure transmission of smart grid fine-grained data as described in any of the above embodiments, which has the beneficial effects of the corresponding method embodiments and will not be repeated here.

[0126] It should be noted that the embodiments of the present application can be further described in the following manner:

[0127] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0128] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operation of the disclosed technical solution based on the prompt message.

[0129] As an optional but non-limiting implementation, in response to a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0130] It is understandable that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0131] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. Within the scope of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0132] In addition, to simplify the description and discussion, and to avoid obscuring the understanding of the embodiments of the present application, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. Furthermore, devices may be shown in block diagram form to avoid obscuring the understanding of the embodiments of the present application, and this also takes into account the fact that the implementation details of these block diagram devices are highly dependent on the platform on which the embodiments of the present application will be implemented (i.e., these details should be fully understood by those skilled in the art). Where specific details (e.g., circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations therefrom. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0133] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the discussed embodiments.

[0134] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of this application.

Claims

1. A method for secure transmission of fine-grained data in a smart grid, characterized in that: Applied to the data sending end, including: Acquiring a plurality of data, and processing the plurality of data based on predetermined parameters to obtain processed data; Performing base conversion on the integer digits in the processed data to obtain first converted data; randomly generating a probability corresponding to the first conversion data, and based on the probability, perturbing the first conversion data according to a predetermined rule to obtain second conversion data; Combining the decimal values in the second converted data and the processed data to obtain target data; Sending the target data to a data receiving end; The processing of the plurality of data based on the predetermined parameters to obtain the processed data includes: performing noise processing on the plurality of data based on the predetermined parameters; integrating the plurality of data after the noise processing to obtain the processed data; The performing noise processing on the plurality of data based on the predetermined parameters includes: generating a sequence corresponding to the plurality of data based on the predetermined parameters; randomly extracting sequence data from the sequence for each data in the plurality of data, and adding the sequence data to obtain initial noise corresponding to the data; multiplying the initial noise by the predetermined parameters to obtain amplified noise corresponding to the data, and adding the amplified noise to the data; Generating the sequences corresponding to the plurality of data based on the predetermined parameters includes: generating two initial sequences conforming to a gamma distribution based on the predetermined parameters; and subtracting the two initial sequences to obtain the sequences corresponding to the plurality of data; The method of perturbing the first conversion data according to a predetermined rule based on the probability to obtain second conversion data includes: in response to determining that the probability is less than a predetermined probability, inverting a character corresponding to a predetermined data bit in the first conversion data to obtain the second conversion data.

2. The method according to claim 1, characterized in that Before performing base conversion on integer bits in the processed data to obtain first converted data, the method includes: The processed data is added to a predetermined value.

3. A method for secure transmission of fine-grained data in a smart grid, characterized in that: Applied to the data receiving end, including: Receive target data sent by the data sender; Performing base conversion on the integer digits of the target data to obtain third converted data; performing frequency statistics on each character at a predetermined position in the third converted data to obtain a statistical result, and determining a deviation of the third converted data based on the statistical result; Based on the deviation, the sum of the characters in the third converted data is added to the deviation to perform deviation correction to obtain final data.

4. The method according to claim 3, characterized in that The determining the deviation of the third conversion data based on the statistical result includes: error={(mp-m1)p / (2p-1)-[m(p-1)+m1]p / (2p-1)}w, Wherein, error is the deviation, m is the total number of data in the third converted data, p is a preset probability, m1 is the statistical number of 1 in the statistical result, and w is a predetermined data bit weight.

5. A secure transmission device for fine-grained data of a smart grid, characterized in that: Applied to the data sending end, including: a processing module configured to acquire a plurality of data, and process the plurality of data based on predetermined parameters to obtain processed data; A first conversion module is configured to perform base conversion on the integer digits in the processed data to obtain first converted data; a perturbation module configured to randomly generate a probability corresponding to the first conversion data, and based on the probability, perturb the first conversion data according to a predetermined rule to obtain second conversion data; a first merging module configured to merge the second converted data and the decimal values in the processed data to obtain target data; A sending module, configured to send the target data to a data receiving end; The processing module is further configured to perform noise processing on the multiple data based on predetermined parameters; and integrate the multiple data after the noise processing to obtain the processed data; The processing module is further configured to generate a sequence corresponding to the plurality of data based on the predetermined parameter; for each data in the plurality of data, randomly extract sequence data from the sequence, and add the sequence data to obtain initial noise corresponding to the data; multiply the initial noise by the predetermined parameter to obtain amplified noise corresponding to the data, and add the amplified noise to the data; The processing module is further configured to generate two initial sequences conforming to the gamma distribution based on the predetermined parameters; subtract the two initial sequences to obtain a sequence corresponding to the plurality of data; The disturbance module is further configured to, in response to determining that the probability is less than a predetermined probability, perform an inversion operation on a character corresponding to a predetermined data bit in the first converted data to obtain the second converted data.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

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