Secure transmission method for fine-grained data of smart power grid and related equipment

Abstract

The invention provides a secure transmission method for fine-grained data of a smart power grid and related equipment, and the method comprises the steps: obtaining a plurality of pieces of data, and processing the plurality of pieces of data based on predetermined parameters to obtain processed data; carrying out system conversion on a numerical value of an integer bit in the processing data to obtain first conversion data; randomly generating a probability corresponding to the first conversion data, and disturbing the first conversion data according to a preset rule based on the probability to obtain second conversion data; combining the numerical values of the decimal digits in the second conversion data and the processing data to obtain target data; and sending the target data to a data receiving end, thereby solving the technical problem in the prior art that the accuracy of the data is influenced after privacy processing is performed on the data, and achieving the purpose of ensuring the accuracy and security of the 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, the traditional power distribution model can no longer adapt to the growing demand for electricity. In order to solve this problem, the intelligent power grid system has received widespread attention and is in a state of rapid development. In the 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 regulation of power. In order to ensure the matching of power supply and demand, the intelligent power data center needs to collect fine-grained power consumption data provided by the smart meter on the user side. However, directly submitting the original power consumption data has the risk of leaking personal privacy, so that key privacy information such as user behavior patterns and living habits can be obtained.

[0003] In order to ensure the privacy of users, a privacy protection mechanism needs to be used to process electricity consumption data during the entire data transmission process. However, after the electricity consumption data is processed by the privacy mechanism, the accuracy of the electricity consumption data aggregation result is low, and the data aggregation result is unusable, which makes it difficult to effectively resolve the contradiction 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 method for secure transmission of fine-grained data of a smart grid, which is applied to a data sending end, and 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 a predetermined rule 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.

[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 in 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 expanded noise corresponding to the data, and adding the expanded noise to the data.

[0008] Optionally, generating the sequence corresponding to the multiple data based on the predetermined parameters includes: generating two initial sequences conforming to the gamma distribution based on the predetermined parameters; and subtracting the two initial sequences to obtain the sequence 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 perturbed 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 a character corresponding to a 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; based on the deviation, adding the 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: ,in, For the deviation, is the total number of data in the third conversion data, is the pre-set probability, is the statistical number of 1 in the statistical result, is the predetermined data bit weight.

[0013] 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; a sending module, configured to send the target data to a data receiving end.

[0014] Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0015] From the above, it can be seen that the secure transmission method and related equipment of fine-grained data of smart grids provided by the present application include obtaining multiple data, processing the multiple data based on predetermined parameters, obtaining processed data, and combining scattered data into a whole, thereby eliminating the scattered nature of multiple data. Performing base conversion on the numerical values ​​of the integer digits in the processed data to obtain first conversion data, so as to facilitate privacy encryption processing of the processed data. Randomly generate the probability corresponding to the first conversion data, and based on the probability, perturb the first conversion data according to predetermined rules to obtain second conversion data, thereby further ensuring the security of the processed data. Combine the second conversion data and the numerical values ​​of the decimal places 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

[0016] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A schematic diagram of a process for securely transmitting fine-grained data of a smart grid according to an embodiment of the present application; Figure 2 A schematic diagram of a data publishing model constructed in an embodiment of the present application; Figure 3 A schematic diagram of another data publishing model constructed in an embodiment of the present application; Figure 4 A schematic diagram of a flow chart of a method for securely transmitting fine-grained data of a smart grid according to another embodiment of the present application; Figure 5 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; Figure 6 A schematic diagram of the structure of a secure transmission device for fine-grained data of a smart grid according to another embodiment of the present application; Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0019] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of 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 can 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.

[0020] As described in the background technology section, in large-scale electricity consumption scenarios, the traditional distribution model can no longer adapt to the growing demand for electricity. In traditional distribution networks, there are problems such as poor power regulation capabilities, slow information flow, and high operating costs. In order to solve this problem, the intelligent power grid system has received widespread attention and is in a state of rapid development. In the intelligent power grid system, the intelligent power data center is the core hub in the power grid, responsible for data analysis and calculation and real-time regulation of electricity. In order to ensure the matching of power supply and demand, the intelligent power data center needs to collect fine-grained power consumption data provided by the smart meter on the user side. Directly submitting the original power consumption data poses the risk of leaking personal privacy. The above 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.

[0021] In order to ensure the privacy of users, a privacy protection mechanism is needed to process electricity consumption data. However, after the electricity consumption data is processed by the privacy mechanism, the data aggregation results obtained by the smart power data center are less accurate and unusable. When using the existing privacy protection model for data privacy protection, the privacy protection model is difficult to effectively resolve the contradiction between privacy protection and data accuracy. The above privacy protection model can be a distributed differential privacy system model, which refers to a system model that performs noise processing on the data before uploading it to the center. This model can ensure that the data center can obtain the data aggregation results but cannot obtain the specific electricity data value of each user.

[0022] In addition, existing literature has adopted data encryption technology solutions such as homomorphic encryption, but the privacy protection solution based on data encryption consumes the computing resources of smart meters. Since the data provided by smart meters is real-time fine-grained electricity consumption data, smart meters need to submit data to the data center multiple times a day. There are solutions using rechargeable battery technology in existing literature, but the use of rechargeable battery technology requires users to purchase rechargeable batteries, which increases the cost of users, and the use of rechargeable batteries has fire safety hazards. Smart meters need to encrypt a large amount of data, which greatly consumes the computing resources of smart meters. Moreover, there are solutions using differential privacy technology in existing literature. The existing solutions do not consider the balance between data availability and data privacy. Some solutions destroy the correlation between data and time during data processing, and the center cannot specifically know the electricity demand of users in the same block at a certain point in time.

[0023] 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: Step 101, acquiring multiple data, and processing the multiple data based on predetermined parameters to obtain processed data.

[0024] In this step, multiple data are obtained, wherein the data may be time-correlated power data in a smart grid. A smart grid refers to an intelligent power grid system based on Internet of Things technology, which mainly includes a communication network and a distribution network. The intelligent dispatch of power resources is realized through data analysis in a data center. If multiple data are directly transmitted, the multiple data are irregular and the number is relatively large, which will cause data receiving pressure at the data receiving end. Therefore, it is necessary to process the multiple data through predetermined parameters to obtain processed data, wherein the processed data can reflect multiple data, synthesize scattered data into a whole, and eliminate the scattered nature of multiple data.

[0025] 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 Define the data set as A, and for each user's data collected at time t, define it 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 in 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.

[0026] Step 102, performing base conversion on the integer digits in the processed data to obtain first converted data.

[0027] In this step, the processed data includes integer digit values ​​and decimal digit values. In order to ensure the transmission security of the subsequent 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 digit values ​​in the processed data are converted into binary values, and the first converted data is obtained. The binary value is represented by multiple 0s and 1s. By changing the binary value, it is convenient to perform privacy encryption processing on the processed data.

[0028] 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.

[0029] In this step, the probability corresponding to the first conversion data is randomly generated. For example, the probability corresponding to the first conversion data can be generated by a random number generator from 0 to 1. Through the probability, it can be determined to perturb the first conversion data according to a predetermined rule to obtain the second conversion data. The original processed data is modified again 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 perturbed according to the predetermined rule, the perturbed first conversion data is converted from a binary value to a decimal value.

[0030] Step 104: Combine the decimal values ​​in the second converted data and the processed data to obtain target data.

[0031] In this step, the decimal values ​​in the second conversion data and the processed data are combined to obtain the target data. The integer values ​​in the target data are modified, and the target data is not completely the same as the aforementioned processed data, so that the determined target data is secure.

[0032] Step 105: Send the target data to a data receiving end.

[0033] In this step, the target data is sent to the data receiving end, where the data receiving end can be a server. The target data is obtained after multiple data are processed by multiple processing technologies, ensuring the accuracy of the subsequent data receiving end's decryption of the data and the security of the data transmission.

[0034] 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 himself are considered untrustworthy. In the above model, all users will independently complete differential privacy processing of data on their own smart meters, use the separability of the Laplace distribution proposed earlier to generate independent Laplace noise, and each user perturbs the data with a certain probability, further disrupting the data and destroying the data rules. Finally, it is published to the aggregator, and the smart power data center performs data analysis, as shown Figure 3 shown.

[0035] Through the above scheme, multiple data are obtained, and the multiple data are processed based on predetermined parameters to obtain processed data, and the scattered data are combined into a whole, eliminating the scatteredness of multiple data. The integer digits in the processed data are converted to obtain the first conversion data, which is convenient for privacy encryption processing of the processed data. The probability corresponding to the first conversion data is randomly generated, and based on the probability, the first conversion data is disturbed according to a predetermined rule to obtain the second conversion data, which further ensures the security of the processed data. The second conversion data and the decimal digits in the processed data are combined to obtain the target data, so that the determined target data is secure. The target data is sent to the data receiving end to ensure the security of data transmission.

[0036] 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. In this embodiment, based on predetermined parameters, multiple data are subjected to noise processing, so that the data after noise processing is different from the original data, thereby ensuring the privacy of multiple data and making the data after noise processing not easy to be cracked. It should be noted that the predetermined parameter can be a predetermined scale parameter, which is determined by the ratio of the predetermined data sensitivity and the predetermined privacy budget value. The multiple data after noise processing are added to integrate the multiple data after noise processing together to obtain processed data, and data integration is performed at the data sending end, and the multiple data are processed at the data sending end, thereby avoiding the subsequent transmission of scattered data to the data receiving end.

[0037] 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 the set of n user noises, is the sum of N-dimensional noise data, are two continuous gamma distributions Discretized sampling of a distribution.

[0038] 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 in 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 expanded noise corresponding to the data, and adding the expanded noise to the data.

[0039] In this embodiment, based on predetermined parameters, a sequence corresponding to multiple data is generated, and the multiple data are converted into an ordered sequence, so that the multiple data become ordered. Noise needs to be added to each data. In order to ensure the randomness of the noise, the sequence data in the sequence is randomly extracted, and each character in the sequence data is added to obtain the initial noise corresponding to the data. The initial noise is multiplied by the predetermined parameter to obtain the expanded noise corresponding to the data. By expanding the noise, the noise is made more prominent. Adding the expanded noise to the data improves the security of the data.

[0040] In some embodiments, generating the sequence corresponding to the multiple data based on the predetermined parameters includes: generating two initial sequences conforming to the gamma distribution based on the predetermined parameters; and subtracting the two initial sequences to obtain the sequence corresponding to the multiple data. 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 you want to get the data of gamma (shape parameter a, scale parameter b), use gamma (shape parameter a, scale parameter 1) to generate data, and then multiply the data by b to get the 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.

[0041] It should also be noted that the gamma distribution uses an improved Laplace mechanism. In the Laplace mechanism, when adding Laplace distribution noise, in order to ensure the accuracy of the aggregation result, that is, the noise sum is zero, it is necessary to use a Laplace probability density function with a location parameter of zero. Based on this feature and several properties related to the Laplace distribution, the Laplace distribution can be decomposed into several gamma distributions. Before the derivation, the three main properties used in the derivation process are listed.

[0042] Property 1: Laplace distribution is a symmetric distribution. When the location parameter is 0, the probability density curve of 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 .

[0043] 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.

[0044] Property 3: Additivity of the gamma distribution, with n random variables ,So .

[0045] The derivation process is as follows: Suppose there are n random variables , n random variables .

[0046] From property 3, we know , .

[0047] From Property 2, we can get: , It follows an exponential distribution.

[0048] From property 1, we can see that .

[0049] therefore, .

[0050] Therefore, the Laplace distribution can be composed of several gamma distributions subtracted from each other.

[0051] 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. 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.

[0052] 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 a predetermined probability, inverting the character corresponding to the predetermined data bit in the first conversion data to obtain the second conversion data. In this embodiment, when the probability is less than the predetermined probability, it is necessary to perform an inversion operation on the character corresponding to the predetermined data in the first conversion data. Exemplarily, the inversion operation is performed on the xth to a+x-1th bits (a bits in total) in the 8-bit binary sequence, ensuring sufficient disturbance of the first conversion data. The bit disturbance algorithm divides the input data into an integer part and a decimal part. The integer part is then binary-encoded and converted into 8-bit binary data. The selected data bits are then disturbed, and the data bits are inverted with a probability of p, thereby changing the data size. Next, the integer part and the decimal part of the disturbance are added to obtain the final disturbance data, so that the first conversion data is fully disturbed.

[0053] In the bit perturbation algorithm, multiple data bits and different probabilities can be perturbed, further disrupting the data pattern in the continuous time period. The model protects the user's privacy while ensuring the availability of the aggregation 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 time t All through disturbance[ ] processing. In the bit perturbation algorithm, you can choose to perturb multiple data bits and different probabilities, further disrupting the data pattern in a continuous time period. The model protects the user's privacy while ensuring the availability of the aggregated results.

[0054] 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: Step 201, receiving target data sent by a data sending end.

[0055] In this step, target data sent by a data sending end is received, wherein the data sending end may be a smart grid.

[0056] Step 202: performing base conversion on the integer digits of the target data to obtain third converted data.

[0057] In this step, since the numerical value of the integer digit of the target data is obtained through conversion, in order to restore the target data to the processing data, it is necessary to convert the numerical value of the integer digit of the target data to obtain the third conversion data, which lays the foundation for restoring the target data to the processing data.

[0058] Step 203: Perform frequency statistics on each character of a predetermined position in the third conversion data to obtain a statistical result, and determine the deviation of the third conversion data based on the statistical result.

[0059] In this step, frequency statistics are performed on each character corresponding to a predetermined position in the third conversion data to obtain a statistical result. The predetermined position is a predetermined data position. Through the statistical result, the deviation of the third conversion data can be determined, and through the deviation, the data position that has been changed in the third conversion data can be known, so as to achieve the purpose of knowing the transformation of the third conversion data.

[0060] It should be noted that the 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 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 the accuracy of data, and information entropy is used to measure the privacy of data. When the two are equal, it is considered that data accuracy and privacy are equivalent, and the privacy budget at this time is a balanced privacy budget.

[0061] Step 204: Based on the deviation, the sum of the characters of the third converted data is added to the deviation to perform deviation correction to obtain final data.

[0062] In this step, based on the deviation, the third conversion data can be corrected, and the sum of the characters of the third conversion data is added to the deviation to correct the deviation and obtain the final data. Data privacy protection also needs to be kept confidential for 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 area can be known. By continuously obtaining the total power consumption of a certain area at different times, the power resources can be scheduled and deployed according to the power consumption. Therefore, the recovered deviation is a total deviation, and each specific data cannot be restored. In summary, the calculated deviation is a deviation of the sum of the characters of the third conversion data. The accuracy of the data received by the receiving end is ensured.

[0063] Through the above scheme, the target data sent by the data sending end is received. The integer digits of the target data are converted into base 1 to obtain the third conversion data, which lays the foundation for restoring the target data to the processing data. The frequency of each character in the third conversion data is counted to obtain the statistical result. Based on the statistical result, the deviation of the third conversion data is determined to achieve the purpose of knowing the transformation of the third conversion data. Based on the deviation, the sum of the characters of the third conversion data is added to the deviation to correct the deviation and obtain the final data, which ensures the accuracy of the data received by the subsequent data receiving end.

[0064] 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 conversion data, is the pre-set probability, is the statistical number of 1 in the statistical result, is the predetermined data bit weight.

[0065] 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 small balls are red and green. Among them, there are b people with green balls. In turn, the color of the small balls owned by these m people is asked, and each person answers with The probability of answering a color different from the color of the ball in hand. After statistics, we know that The individual has the green ball, 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 ball 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.

[0066] The probability of 1 after statistics can be expressed as: .

[0067] The probability of 0 after statistics can be expressed as: .

[0068] Establish the likelihood function: .

[0069] Take the logarithm: .

[0070] make , The maximum likelihood estimate of , hour: .

[0071] So the number of 1s before the disturbance is , the number of 0 is .

[0072] Therefore, the deviation can be calculated: .

[0073] The design of the bit perturbation algorithm is based on the above theoretical analysis. It should be pointed out that the algorithm and privacy budget The relationship is Generally speaking, the privacy budget is usually between 0 and 1, and privacy is the value that guarantees 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 ​​of 0 to 0.5 show an odd symmetric relationship. The range is [0,0.5).

[0074] 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 to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0075] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] 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.

[0077] refer to Figure 5 , applied to a data transmitting end, the secure transmission device for fine-grained data of a smart grid comprises: The processing module 10 is configured to obtain a plurality of data, and process the plurality of data based on a predetermined parameter to obtain processed data; A first conversion module 20 is configured to perform base conversion on the integer values ​​in the processed data to obtain first converted data; The disturbance module 30 is configured to randomly generate a probability corresponding to the first conversion data, and based on the probability, disturb the first conversion data according to a predetermined rule to obtain second conversion data; A first merging module 40 is configured to merge the second converted data and the numerical value of the decimal place in the processed data to obtain target data; The sending module 50 is configured to send the target data to a data receiving end.

[0078] Through the above device, multiple data are obtained, and the multiple data are processed based on predetermined parameters to obtain processed data, and the scattered data are combined into a whole, eliminating the scattered nature of the multiple data. The integer digits in the processed data are converted to a base to obtain the first converted data, which is convenient for privacy encryption processing of the processed data. The probability corresponding to the first converted data is randomly generated, and based on the probability, the first converted data is disturbed according to a predetermined rule to obtain the second converted data, which further ensures the security of the processed data. The second converted data and the decimal digits in the processed data are combined to obtain the target data, so that the determined target data is secure. The target data is sent to the data receiving end to ensure the security of data transmission.

[0079] 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.

[0080] 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 of 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 expanded noise corresponding to the data, and add the expanded noise to the data.

[0081] 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 multiple data.

[0082] 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 converting the integer bits in the processed data to obtain the first converted data.

[0083] 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 conversion data to obtain the second conversion data.

[0084] 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.

[0085] refer to Figure 6 The secure transmission device for fine-grained data of the smart grid is applied to a data sending end, and includes: The receiving module 60 is configured to receive target data sent by the data sending end; The second conversion module 70 is configured to perform base conversion on the integer digits of the target data to obtain third converted data; The determination module 80 is configured to perform frequency statistics on each character of a predetermined position in the third conversion data to obtain a statistical result, and determine the deviation of the third conversion data based on the statistical result; The correction module 90 is configured to add the character sum of the third conversion data to the deviation to perform deviation correction based on the deviation to obtain final data.

[0086] The target data sent by the data sending end is received by the above device. The integer digit value of the target data is converted into a base to obtain the third conversion data, which lays the foundation for restoring the target data to the processing data. The frequency of each character in the third conversion data is counted to obtain a statistical result. Based on the statistical result, the deviation of the third conversion data is determined to achieve the purpose of knowing the transformation of the third conversion data. Based on the deviation, the third conversion data is corrected to obtain the fourth conversion data, which ensures the accuracy of the fourth conversion data. The fourth conversion data is combined with the decimal digit value of the target data to obtain the final data, which ensures the accuracy of the subsequent data receiving end in decrypting the data.

[0087] In some embodiments, the determining module 80 is further configured to ,in, For the deviation, is the total number of data in the third conversion data, is the pre-set probability, is the statistical number of 1 in the statistical result, is the predetermined data bit weight.

[0088] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0089] The 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.

[0090] 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 executable on the processor, wherein when the processor executes the program, the method for secure transmission of fine-grained data of a smart grid as described in any of the above embodiments is implemented.

[0091] Figure 7 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and 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 connected to each other through the bus 1050 in the device.

[0092] The processor 1010 can be implemented by 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.

[0093] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0094] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0095] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0096] The bus 1050 includes a path that transmits 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 ).

[0097] 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 the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0098] The electronic device of the above-mentioned embodiment is used to implement the corresponding method for securely transmitting fine-grained data of the smart grid in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0099] 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.

[0100] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, read-only compact disk read-only memory (CD-ROM), digital versatile disk (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.

[0101] The computer instructions stored in the storage medium of the above embodiment 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, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0102] 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 executed on a computer, the computer executes the method for secure transmission of fine-grained data of a smart grid as described in any of the above embodiments, which has the beneficial effects of the corresponding method embodiments and will not be repeated here.

[0103] It should be noted that the embodiments of the present application can be further described in the following manner: 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.

[0104] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0105] As an optional but non-limiting implementation, in response to receiving the 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. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0106] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0107] A person 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. In line with the concept 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.

[0108] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0109] Although the present application has been described in conjunction with specific embodiments of the present application, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0110] 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 the present 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: Acquire multiple data, and process the multiple 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 second converted data and the decimal values ​​in the processed data to obtain target data; The target data is sent to a data receiving end.

2. The method according to claim 1, characterized in that The processing of the plurality of data based on the predetermined parameters to obtain processed data comprises: Based on predetermined parameters, performing noise processing on the plurality of data; The multiple data after the noise addition processing are integrated to obtain the processed data.

3. The method according to claim 2, characterized in that The performing noise addition processing on the plurality of data based on the predetermined parameters comprises: Based on the predetermined parameters, generating a sequence corresponding to the plurality of data; For each data among the plurality of data, randomly extract sequence data in the sequence, and add the sequence data to obtain initial noise corresponding to the data; The initial noise is multiplied by the predetermined parameter to obtain an enlarged noise corresponding to the data, and the enlarged noise is added to the data.

4. The method according to claim 3, characterized in that The generating, based on the predetermined parameters, a sequence corresponding to the plurality of data comprises: Based on the predetermined parameters, two initial sequences conforming to the gamma distribution are generated; The two initial sequences are subtracted to obtain sequences corresponding to the multiple data.

5. The method according to claim 1, characterized in that Before converting the integer bits in the processed data to obtain first converted data, the method includes: The processed data is added to a predetermined value.

6. The method according to claim 1, characterized in that The 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, a character corresponding to a predetermined data bit in the first converted data is inverted to obtain the second converted data.

7. 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 conversion data to obtain a statistical result, and determining a deviation of the third conversion data based on the statistical result; Based on the deviation, the sum of the characters in the third conversion data is added to the deviation to perform deviation correction to obtain final data.

8. The method according to claim 7, characterized in that The 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 conversion data, is the pre-set probability, is the statistical number of 1 in the statistical result, is the predetermined data bit weight.

9. 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 is configured to obtain a plurality of data, and process the plurality of data based on a predetermined parameter to obtain processed data; A first conversion module is configured to perform base conversion on the integer value in the processed data to obtain first converted data; a disturbance module, configured to randomly generate a probability corresponding to the first conversion data, and based on the probability, perform disturbance on the first conversion data according to a predetermined rule to obtain second conversion data; A first merging module is configured to merge the second converted data and the numerical value of the decimal place in the processed data to obtain target data; The sending module is configured to send the target data to a data receiving end.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

Images