A method for encrypting and storing enterprise privacy data

Abstract

The present application relates to the field of digital data encryption technology, and specifically to an encryption storage method for enterprise privacy data. The method converts the enterprise privacy data to be encrypted into a binary sequence and then segments it, converts each segment of binary data into a two-dimensional matrix by base conversion, sets the classification category according to the value of the classification index of the data point in the two-dimensional matrix and classifies all data points, generates a result matrix with the same classification number and each data point is binarized, performs Radon transformation on each result matrix to determine its highlight points in the Radon parameter space, scrambles the positions of the highlight points to achieve scrambling of the binarized data points in the result matrix, thereby achieving scrambling of each data point in the two-dimensional matrix formed by the enterprise privacy data, converts each scrambled data point in the two-dimensional matrix into a binary sequence fragment and splices them in a set order, obtains the encrypted ciphertext of the enterprise privacy data, and achieves efficient and secure encryption of the enterprise privacy data.

Description

Technical Field

[0001] The present application relates to the technical field of digital data encryption, and in particular to an encryption storage method for enterprise privacy data. Background Art

[0002] Enterprise privacy data usually refers to personnel information data, customer data, capital flow data, internal communication data, etc. that are not easy for enterprises to disclose. For the compliance of enterprises, enterprises need to protect privacy data in accordance with relevant regulations. The protection process of privacy data includes encryption, transmission and storage.

[0003] Due to the diversity of enterprise privacy data, the commonly used encryption methods for enterprise privacy data are mainly divided into data scrambling and data encryption. The data scrambling method is to change the position of the data through mathematical or geometric transformation, so that the scrambled data is obviously different from the original data to achieve the purpose of encryption. However, since the content of the enterprise privacy data is random and the data type is not fixed, it may cause the scrambled data to be similar to the original data, that is, the encryption effect of enterprise privacy data scrambling is unstable, which will lead to poor privacy and security of the encrypted data obtained after encrypting some enterprise privacy data. The data encryption method converts plaintext data into unrecognizable and incomprehensible ciphertext, but this method is often complex in operation, and the information entropy of the obtained data is large, which makes data storage difficult, and the encryption security of enterprise privacy data is insufficient and the encryption process is complex and inefficient. Summary of the invention

[0004] This application provides an encrypted storage method for enterprise privacy data, which is used to solve the problem that the existing technology cannot efficiently and securely complete the encrypted storage of enterprise privacy data. The technical solution adopted is as follows:

[0005] The present invention provides a method for encrypting and storing enterprise private data, comprising the following steps:

[0006] Binary encode the enterprise privacy data to be encrypted to generate a binary sequence, segment the binary sequence according to the set number of segment bits, and perform decimal conversion on each segment of the binary sequence to obtain a decimal number equal to the number of segments;

[0007] Put all decimal numbers into the two-dimensional matrix in the set order, and after all decimal numbers are put into the two-dimensional matrix, put 0 at all data point positions in the two-dimensional matrix where no data is put in, to complete the construction of the two-dimensional matrix;

[0008] Calculate the classification index of each data point in the two-dimensional matrix, set the number of categories based on the values ​​of the classification indexes of all data points, the value ranges of the classification indexes under each category do not overlap, and the sum of the value ranges of the classification indexes under all categories completely covers the value ranges of the classification indexes of all data points;

[0009] All data points in the two-dimensional matrix are screened according to the value range of the classification index under each category, and the corresponding result matrix under each category is obtained through the screening results of the data points;

[0010] Screening the highlight points according to the distribution characteristics of the highlight points of each result matrix in the Radon parameter space, and obtaining a highlight point sequence based on the screening results;

[0011] A chaotic sequence is generated through chaotic mapping, and the highlight point sequence is scrambled with the chaotic sequence, thereby changing the position of the binary data point with a value of 1 in each result matrix, completing the scrambling of the binary data points in each result matrix, determining the corresponding position of each data point in the two-dimensional matrix according to the position of the scrambled binary data point in all result matrices, completing the scrambling of the data points in the two-dimensional matrix, converting each data point in the scrambled two-dimensional matrix into binary and splicing them, using the encryption key to obtain the encrypted ciphertext of the enterprise privacy data, and storing the obtained encrypted ciphertext.

[0012] Preferably, before segmenting the binary sequence according to the set number of paragraph bits, the method further includes performing an operation of padding 0 before the binary sequence:

[0013]

[0014] Among them, A represents the number of bits that need to be padded with 0 before the binary sequence. Indicates the number of bits in the set paragraph, B indicates the overall length of the binary sequence, Represents the floor function.

[0015] Preferably, the calculation method of the classification index of each data point in the two-dimensional matrix is:

[0016]

[0017] Among them, C represents the classification index of the data point at the center point of the set neighborhood, norm() represents the normalization function, n represents the side length of the set neighborhood, and D represents the value of the data point at the center point in the set neighborhood. It represents the gray value of the i-th data point in the set neighborhood excluding the data point at the center point. Represents the average difference between the value of the data point at the center point in the set neighborhood and the values ​​of other data points in the set neighborhood.

[0018] Preferably, the method of obtaining the result matrix corresponding to each category through the screening results of the data points is:

[0019] For any classification, determine the value range of the classification index under each classification. If the classification index of a data point in the two-dimensional matrix is ​​within the value range, then the data point is retained, otherwise the data point is set to 0;

[0020] The values ​​of all retained non-zero data points are set to 1, and binarization is performed to obtain the result matrix corresponding to each classification.

[0021] Preferably, the method for acquiring the highlight point sequence is:

[0022] Perform Radon transform on each result matrix to determine the highlight points of each result matrix in the Radon parameter space, filter the highlight points of the result matrix in the Radon parameter space with the highlight point threshold corresponding to each result matrix, map the filtered highlight points corresponding to all result matrices to the same Radon parameter space, and sort all the filtered highlight points according to the order of their appearance to obtain a highlight point sequence.

[0023] Preferably, the calculation method of the highlight point threshold is:

[0024]

[0025] in, represents the highlight brightness threshold of the i-th result matrix, represents the number of binary data points with a value of 1 in the i-th result matrix, Represents the number of data points in the two-dimensional matrix, and W represents the empirical highlight threshold, which is set based on experience.

[0026] Preferably, the method for generating a chaotic sequence by chaotic mapping is:

[0027] by Mapping implements chaotic mapping:

[0028]

[0029] in, represents the value obtained after n iterations in the chaotic sequence, represents the value obtained after n-1 iterations in the chaotic sequence, is a controllable parameter;

[0030] set up The number of iterations of the chaos mapping model is ,by Chaotic mapping model After iterations, each value in the obtained chaotic sequence is multiplied by v and rounded down, and the number with a rounded value of 0 is reassigned to 1 to obtain the final chaotic sequence; is the initial iteration number, and p is the number of the first items removed from the chaotic sequence.

[0031] Preferably, the method of scrambling the highlight sequence with a chaotic sequence is:

[0032] The highlight point sequence is connected head to tail to form a ring sequence, and then, according to the order of position arrangement of all values ​​in the chaotic sequence, the highlight points whose sorting numbers are the same as the values ​​at each position in the chaotic sequence are extracted from the highlight point sequence in sequence, and after each extraction, the extracted and filtered highlight points are removed from the highlight point sequence to obtain a scrambled highlight point sequence;

[0033] After the highlight sequence is connected end to end to form a circular sequence, the sequence number of the i-th highlight selected in the highlight sequence is , ,in, is the number of serial number cycles, For highlight sequence The number of remaining filtered highlight points in the highlight point sequence after the extraction of the filtered highlight points.

[0034] Preferably, the process of obtaining the encrypted ciphertext of the enterprise privacy data includes:

[0035] All data points in the scrambled two-dimensional matrix are converted from decimal to binary, and all the data points converted to binary in the scrambled two-dimensional matrix are concatenated according to the set order in which the data is placed when constructing the two-dimensional matrix to obtain the final encrypted ciphertext.

[0036] Preferably, the encryption key can be specifically divided into an encryption key and an auxiliary key, wherein the encryption key is , the subsidiary key is , is the initial value of the chaotic map.

[0037] The beneficial effects of this application are:

[0038] First, the present application converts the enterprise privacy data to be encrypted into binary sequence data, and then segments the binary sequence data according to a set length and converts the binary data in each segment into decimal data to obtain a decimal number with the same number of segments. Through the above-mentioned base conversion process, the binary number is converted into a decimal number, which can reduce the amount of calculation for subsequent data processing, improve the efficiency of encrypted storage of enterprise privacy data, and improve the processing ability of computer equipment for enterprise privacy data; secondly, the decimal numbers are placed in a two-dimensional matrix in a set order to complete the construction of the two-dimensional matrix, and the classification category is set according to the value range of the classification indicator of each data point in the two-dimensional matrix, and all data points in the two-dimensional matrix are classified to generate the same The result matrix corresponding to the number of classification categories and each data point in which has been binarized is obtained. In this way, the data points in the two-dimensional matrix are analyzed for significant features based on the value range of the classification index of each category, so as to realize the analysis of the privacy degree of different data contents in the enterprise privacy data; after that, each result matrix is ​​subjected to Radon transformation to determine its highlight points in the Radon parameter space, the highlight points are screened and then the positions of all the screened highlight points are scrambled to realize the scrambling of the binarized data points in the result matrix, thereby realizing the scrambling of each data point in the two-dimensional matrix, converting each scrambled data point in the two-dimensional matrix into a binary sequence fragment and splicing it in a set order, so as to obtain the final encrypted ciphertext. The method of the present application adds structural factors to the data to be encrypted to construct a two-dimensional matrix, utilizes the characteristic that a single highlight point in the Radon parameter space corresponds to multiple data points in the two-dimensional matrix, and realizes the batch rapid scrambling of a large number of data points in the two-dimensional matrix by scrambling the highlight points in the Radon parameter space, thereby realizing the efficient and secure encryption of enterprise privacy data. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a method for encrypting and storing enterprise privacy data in the present application. DETAILED DESCRIPTION

[0040] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the encryption storage method of enterprise privacy data proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0041] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0042] A method for encrypting and storing enterprise privacy data of the present application is described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] The present application provides an embodiment of a method for encrypting and storing enterprise privacy data, and its overall process is as follows: Figure 1 As shown, the specific process is as follows:

[0044] Step S001, organize the enterprise privacy data and divide it into segments, and convert the binary data in each segment into decimal data.

[0045] In view of the situation where an enterprise has different types of privacy data such as text data, digital data, and graphic data, in this application, the enterprise privacy data is first converted by base conversion so that different types of privacy data can be encrypted and stored.

[0046] Furthermore, computers can only recognize data 0 and 1 when transmitting and storing corporate privacy data, so this embodiment first encodes and converts the corporate privacy data that needs to be encrypted to form a binary sequence data composed of 0 and 1. In order to facilitate the subsequent accurate analysis of the associated features in the corporate privacy data, thereby realizing the classification scrambling of the corporate privacy data, this embodiment divides the binary data according to the set number of paragraph bits, and divides the binary sequence into several segments, each segment length is the set number of paragraph bits. It is easy to understand that the value of the number of paragraph bits can be any integer value greater than 1. Considering that a byte in computer information technology includes 8 bits in binary, that is, 1Byte=8bit, the preferred number of paragraph bits in this embodiment is 8. In other embodiments, the implementer can set the size of the number of paragraph bits according to the specific type of corporate privacy data, and this application does not make any special restrictions on this.

[0047] At the same time, we also take into account that the binary sequence is not necessarily exactly divisible by 8, so before setting the specific value of the paragraph digits and dividing the segments, we need to perform a dimensionality increase operation on the binary sequence, that is, fill the front of the binary sequence with 0.

[0048] It should be noted that, in the present application, encrypted storage of corporate privacy data is to convert the privacy data into binary. In order to obtain an integer number of segmented results of the same length and not to change the original content of the corporate privacy data, in this embodiment, 0 is padded in front of the first element of the binary sequence.

[0049] Furthermore, the number of bits that need to be padded with zeros is calculated as follows:

[0050]

[0051] Among them, A represents the number of bits that need to be padded with 0 before the binary sequence. Indicates the number of paragraphs set. In this embodiment , B represents the overall length of the binary sequence, Represents the floor function.

[0052] Through the above-mentioned dimension-increasing and 0-padding operations, it can be ensured that the binary sequence is exactly divisible by 8, and then the binary sequence after the dimension-increasing and 0-padding operations is segmented to obtain a number of segments, each of which contains 8-bit binary numbers.

[0053] The 8-bit binary number in each segment is converted into a decimal system to obtain a number of decimal numbers equal to the number of segments. Since the conversion between binary and decimal is a prior art, it will not be described in detail in this embodiment.

[0054] Since the decimal number in this embodiment is converted from an 8-bit binary number, combined with the prior art of binary-to-decimal conversion, it can be known that the value range of the decimal number converted from the 8-bit binary number is [0, 255].

[0055] It should be noted that by converting binary numbers into decimal numbers through the above-mentioned base conversion process, the amount of computation required for subsequent data processing can be reduced, the efficiency of encrypted storage of corporate privacy data can be improved, and the processing capabilities of computer equipment for corporate privacy data can be improved.

[0056] Step S002, putting all decimal data into a two-dimensional matrix in a set order to complete the construction of the two-dimensional matrix, and classifying the data points in the two-dimensional matrix according to the classification index of each data point in the two-dimensional matrix.

[0057] For corporate privacy data, there may be identical or similar data contents, such as work departments and work contents in employee information. After the corporate privacy data is converted into a binary sequence, the binary numbers in the segments where such identical or similar data contents are located are relatively close. Such identical or similar data contents should be processed differently in the subsequent scrambling and encryption process to improve the security of corporate privacy data.

[0058] Furthermore, in order to further analyze the correlation characteristics between the segmented selected information in the enterprise privacy data, the converted enterprise privacy data is converted from binary to decimal, and the correlation characteristics of the segmented converted enterprise privacy information data are analyzed to achieve the classification of the enterprise privacy data.

[0059] Specifically, each decimal number is converted from a binary number in a segment, and each segment is obtained by segmenting a complete binary sequence, so the obtained segments have a sequence. According to the sequence of each segment, the decimal numbers are sorted, and according to the total number of decimal numbers obtained, a two-dimensional matrix of M rows and N columns is set. In this embodiment, it is preferred to set the sequence from left to right and from top to bottom, and put all the decimal numbers in order. In the two-dimensional matrix of , of course, in other embodiments, any other feasible setting order can be adopted. Considering that the total number of decimal numbers obtained is not necessarily exactly equal to Therefore, after all the decimal numbers are put into the two-dimensional matrix, if there are still some elements in the last row of the two-dimensional matrix that have not been put into the decimal number, then these elements that have not been put into the decimal number are set to 0, that is, 0 is put into these elements that have not been put into the decimal number.

[0060] The number of rows in a two-dimensional matrix With the number of columns There is no special requirement for the specific value of , but considering the convenience of subsequent data processing, this embodiment combines the total number of decimal numbers obtained and converts and The values ​​of are set as close as possible so that the resulting two-dimensional matrix is ​​as square as possible or a rectangle close to a square.

[0061] In this way, the enterprise privacy data is converted into a two-dimensional matrix that is easy to analyze through the above processing flow. The elements in the obtained two-dimensional matrix are composed of decimal numbers and 0, and we get Data points, because the specific values ​​of each data point are different, the degree of difference between its value and the values ​​of other surrounding data points is also different. The data points can be classified according to the difference in value between each data point and the surrounding data points, thereby realizing the classification processing of different data contents in the enterprise privacy data.

[0062] Specifically, for any data point in the two-dimensional matrix, take each data point in the two-dimensional matrix as the center and take the size In this embodiment, the value of n is taken as the empirical value 5, and the data point is calculated by The size of the data point is determined by the difference in the values ​​of other data points in the neighborhood:

[0063]

[0064] Among them, C represents the classification index of the data point at the center point of the set neighborhood, norm() represents the normalization function, n represents the side length of the set neighborhood, and D represents the value of the data point at the center point in the set neighborhood. It represents the value of the i-th data point in the set neighborhood excluding the data point at the center point. Represents the average difference between the value of the data point at the center point in the set neighborhood and the values ​​of other data points in the set neighborhood;

[0065] in, It represents the information difference characteristics after the enterprise privacy data is segmented. The greater the difference between the data content corresponding to the center point and the other data points in the neighborhood in the enterprise privacy data, the more different the value between the center point and the other data points, and the larger the value of C.

[0066] Specifically, after determining the classification index of each data point in the two-dimensional matrix, each data point can be classified according to the specific value of the classification index. The number of classifications can be set according to demand, combined with the actual value range of the classification index C. In this embodiment, the preferred number of categories is 10, and the value ranges of the classification indicators of each category are ,in, , The value of satisfies .

[0067] According to the specific value range of each classification index, each data point in the two-dimensional matrix can be divided into a set number of categories, which is 10 categories in this embodiment. In other embodiments, the implementer can set the number of categories according to the number of decimal numbers converted from the enterprise privacy data, or the value range of the decimal number, and this application does not impose any special restrictions on this. Therefore, through the above steps, the classification of enterprise privacy data is achieved based on the value differences between data points.

[0068] Step S003, according to the value range of the classification index under each category, each data point in the two-dimensional matrix is ​​screened, and the screened data points are binarized to obtain a plurality of result matrices having the same number as the classification categories.

[0069] Here, the purpose of screening each data point in the two-dimensional matrix is ​​to determine the data points with significantly different characteristics in the two-dimensional matrix. Such data points are more likely to correspond to data content with a higher degree of privacy in the enterprise privacy data.

[0070] In the 10 classification results of the two-dimensional matrix, each type of data point corresponds to different data content in the enterprise privacy data, and different data contents have different degrees of privacy. Therefore, it is considered to analyze the significant characteristics of the data points in the two-dimensional matrix based on the value range of the classification indicators of each category.

[0071] Specifically, the value range of the classification index is Take 2D matrix as an example, and filter it. If the classification index of the data point in the 2D matrix is If the data point is within the range, the data point is retained, otherwise the data point is set to 0, and then the values ​​of all the retained non-0 data points are set to 1, and the result matrix corresponding to the classification category and after binarization is obtained.

[0072] Repeat the above binarization process to obtain a result matrix for each category, and the entire two-dimensional matrix can obtain 10 binarized result matrices.

[0073] Step S004, perform Radon transform on each result matrix to determine the highlight points of each result matrix in the Radon parameter space, filter the highlight points of the result matrix in the Radon parameter space with the highlight point threshold corresponding to each result matrix, map the filtered highlight points corresponding to all result matrices to the same Radon parameter space, and sort the filtered highlight points according to the order of appearance to obtain a highlight point sequence.

[0074] After screening in step S003, information data with significant difference characteristics in the enterprise privacy data can be obtained, so as to facilitate the subsequent relative analysis of information characteristics of the segmented enterprise privacy data in the Radon parameter space.

[0075] Specifically, for any result matrix, each binary data point on the result matrix is ​​regarded as a pixel point, that is, the result matrix is ​​regarded as a binary grayscale image, and then the result matrix is ​​transformed by Radon, and the binary data points with a value of 1 are mapped to the Radon parameter space.

[0076] Among them, each binary data point with a value of 1 on the result matrix will form a straight line in the Radon parameter space after Radon transformation, and the corresponding straight lines in the Radon parameter space will intersect at the same point to form a highlight point. The more binary data points with a value of 1 that form a straight line on the result matrix, the brighter the highlight point formed in the corresponding Radon parameter space will be due to the more straight lines intersecting the highlight point. Therefore, the brightness value of the highlight point is the number of straight lines intersecting at the highlight point, which is also the number of binary data points with a value of 1 on the same straight line in the result matrix.

[0077] It is easy to understand that all the binary data points with a value of 1 in each result matrix may not be located on more than one straight line, but some of the binary data points with a value of 1 form a straight line, while the other binary data points with a value of 1 form another straight line, and the number of straight lines that can be formed is also uncertain. Then, the binary data points with a value of 1 in the result matrix will form a number of highlight points in the Radon parameter space that is the same as the number of straight lines, and because each highlight point is formed by the intersection of different numbers of straight lines, and is also formed by different numbers of binary data points with a value of 1 in the result matrix being on the same straight line, the brightness value of each highlight point is also different.

[0078] Since the total number of binary data points with a value of 1 in each result matrix is ​​different, the amount of data contained in each result matrix is ​​also different. Therefore, this embodiment sets a highlight brightness threshold for each result matrix to screen all highlight points formed in the Radon parameter space:

[0079]

[0080] in, represents the highlight brightness threshold of the i-th result matrix, represents the number of binary data points with a value of 1 in the i-th result matrix, represents the number of data points in the two-dimensional matrix, W represents the empirical highlight point threshold, and in this embodiment, the size of W is taken from the empirical value. , min() is the minimum value function. It is easy to understand that in this embodiment .

[0081] The highlight brightness threshold of each result matrix is ​​used to screen the highlight points formed by the result matrix in the Radon parameter space, and the highlight points whose brightness values ​​are greater than the highlight brightness threshold are determined. The highlight points formed by all result matrices in the Radon parameter space are screened, and then all the screened highlight points are mapped to the same Radon parameter space, and all the screened highlight points are sorted according to the coordinate values ​​of the highlight points to obtain a highlight point sequence. ,in, , , They respectively represent the first, second, and Vth highlight points selected from the highlight point sequence.

[0082] In this embodiment, the method for sorting all the selected highlight points is specifically to sort them from top to bottom and from left to right according to the coordinates of each selected highlight point, so as to obtain a highlight point sequence.

[0083] Step S005, generate a chaotic sequence and scramble the highlight point sequence with the chaotic sequence, perform inverse Radon transform on the scrambled highlight points to scramble all data points in the two-dimensional matrix, obtain the scrambled two-dimensional matrix, convert all data points in the scrambled two-dimensional matrix from decimal to binary, and splice all the data points converted to binary in the scrambled two-dimensional matrix according to the set order in which the data is placed when constructing the two-dimensional matrix, to obtain the final encrypted ciphertext.

[0084] Here, the highlight point sequence is scrambled using a chaotic sequence, and the data points in the two-dimensional matrix are scrambled using the inverse Radon transform. The higher the scrambling degree of the highlight point sequence, the higher the scrambling degree of the data points in the two-dimensional matrix. Correspondingly, the higher the degree of scrambling of different data contents in the enterprise privacy data, the higher the security.

[0085] Specifically, in the highlight point sequence formed by the screened highlight points, since the position of each highlight point corresponds to the position of all binary data points with a value of 1 in the result matrix that form the highlight point and are located on the same straight line, if the position of each highlight point in the highlight point sequence is replaced, the position of all binary data points with a value of 1 that form each highlight point in the result matrix and are located on the same straight line will also change, and the position of each data point in the two-dimensional matrix corresponding to each binary data point with a value of 1 in the result matrix will also change. Therefore, by replacing the positions of each screened highlight point in the highlight point sequence, the position replacement of each data point in the two-dimensional matrix can be finally achieved.

[0086] Specifically, firstly, a chaotic sequence is generated by chaotic mapping. Mapping implements chaotic mapping:

[0087]

[0088] in, represents the value obtained after n iterations in the chaotic sequence, represents the value obtained after n-1 iterations in the chaotic sequence, is a controllable parameter. Since this embodiment is to realize chaotic mapping, the controllable parameter The value range is .

[0089] In controllable parameters The value range is When the system enters a chaotic state, In order to prevent the chaotic sequence from being highly similar to the original highlight point sequence, the first p items of the chaotic sequence are removed. In this embodiment, the empirical value of p is 10.

[0090] Furthermore, it can be determined The number of iterations of the chaos mapping model ,in, is the initial number of iterations, the number of iterations It is the same as the total number V of highlight points selected from the highlight point sequence.

[0091] The purpose of this application is to realize the encrypted storage of corporate privacy data. Therefore, each value in the generated chaotic sequence is used to reorder the front and back positions of each selected highlight in the highlight sequence. The minimum value of the front and back positions is 1 and the value of each position is an integer. Therefore, in the following Chaotic mapping model After iterations, this embodiment further multiplies each value in the obtained chaotic sequence by V and rounds down, and reassigns the number with a rounded value of 0 to 1, so that each value in the chaotic sequence is an integer and not less than 1.

[0092] It can be understood that, although after the processing of multiplying all values ​​in the chaotic sequence by V and rounding down and reassigning 0 numbers to 1, each value in the chaotic sequence is an integer not less than 1, there may still be multiple identical values ​​in the chaotic sequence.

[0093] For example, assuming that there are 5 selected highlight points in the obtained highlight point sequence, then the highlight point sequence is , the corresponding chaotic sequence is a sequence of 5 values ​​not less than 1. Assuming the chaotic sequence is (3, 1, 3, 4, 3), the values ​​at each position have the following meanings: the third highlight point selected from the highlight point sequence Place it at the first position, and select the first highlight point in the highlight point sequence. Place it in the second position and select the third highlight point in the highlight point sequence Place it in the third position and select the fourth highlight point in the highlight point sequence Place it in the 4th position, and select the third highlight point in the highlight point sequence Placed at 5th position.

[0094] It can be seen that at this time, there is an unreasonable situation that the same selected highlight point in the highlight point sequence is placed in different positions. Therefore, in order to avoid this situation, this embodiment connects the highlight point sequence end to end to form a ring sequence, and then according to the corresponding chaotic sequence (3, 1, 3, 4, 3) the highlight point sequence that has been connected end to end to form a ring sequence is The specific process of scrambling is as follows:

[0095] According to the chaotic sequence (3, 1, 3, 4, 3), the highlight point sequence is The third highlight selected Put it in the first place, and the scrambled highlight sequence is , the sequence of highlight points after the first culling operation changes to ;

[0096] According to the chaotic sequence (3, 1, 3, 4, 3), the highlight sequence after the first elimination operation is removed during the second scrambling. The first highlight selected in Placed in the second position, the scrambled highlight sequence is , the sequence of highlight points after the second culling operation changes to ;

[0097] According to the chaotic sequence (3, 1, 3, 4, 3), the highlight sequence after the second elimination operation is The third highlight selected Placed in the third position, the scrambled highlight sequence is , the sequence of highlight points after the third culling operation changes to ;

[0098] According to the chaotic sequence (3, 1, 3, 4, 3), the highlight sequence after the third elimination operation should be The fourth highlight point is placed in the fourth position, but it is obvious that the highlight point sequence after the third elimination operation Only two selected highlight points are included in the sequence. However, since the highlight point sequence has been connected end to end to form a ring sequence, the highlight point sequence after the third elimination operation is middle, This is the second highlight selected. Then the third highlight point is selected. This is the third highlight selected. The fourth highlight point is selected later, so when the fourth scrambling is performed, the highlight point sequence after the third elimination operation is The operation of placing the fourth selected highlight point in the fourth position is to place the selected highlight point Placed in the fourth position, the scrambled highlight sequence is , the sequence of highlight points after the fourth culling operation changes to ;

[0099] According to the chaotic sequence (3, 1, 3, 4, 3), the highlight sequence after the fourth elimination operation should be The third highlight point selected is placed in the fifth position. Referring to the fourth scrambling operation, since the highlight point sequence has been connected end to end to form a ring sequence, the highlight point sequence after the fourth elimination operation is The third highlight selected is , at this time, the final scrambled highlight sequence is .

[0100] It can be seen that in the fourth scrambling, the highlight sequence after the third elimination operation middle The sorting sequence number is actually equal to 2+1*2, where the first 2 is the sequence of highlight points after the third elimination operation. middle The original sequence number, 1 is the number of sequence cycles, and the second 2 is the sequence of highlights after the third elimination operation. The number of highlight points filtered out; Similarly, in the fifth scrambling, the sequence of highlight points after the fourth elimination operation is middle The sorting sequence number is actually equal to 1+2*1, where the first 1 is the highlight point sequence after the fourth elimination operation. middle The original serial number, 2 is the number of serial number cycles, and the second 1 is the highlight point sequence after the fourth elimination operation The number of highlight points filtered out.

[0101] Specifically, after the highlight point sequence is connected end to end to form a circular sequence, the sequence number of the i-th highlight point selected in the highlight point sequence is , ,in is the number of serial number cycles, For highlight sequence The number of remaining filtered highlight points in the highlight point sequence after the extraction of the filtered highlight points.

[0102] In this way, the scrambling of the highlight point sequence according to the chaotic sequence is achieved, and the inverse Radon transform of the scrambled highlight points can be used to redetermine the position of each data point in the two-dimensional matrix corresponding to the scrambled highlight points, thereby completing the scrambling of all data points in the two-dimensional matrix and obtaining the scrambled two-dimensional matrix. All data points in the scrambled two-dimensional matrix are converted from decimal to binary, and all data points converted to binary in the scrambled two-dimensional matrix are pieced together in the set order of data placed when constructing the two-dimensional matrix to obtain the final encrypted ciphertext of the enterprise privacy data.

[0103] Secondly, after obtaining the encrypted ciphertext of the enterprise's private data, the encrypted ciphertext is stored in the enterprise database to complete the storage of the obtained encrypted ciphertext. At this point, the encrypted storage of the enterprise's private data is completed. The encryption key in the encryption process can be specifically divided into an encryption key and an auxiliary key. The encryption key is , the subsidiary key is ,in, is the controllable parameter of the chaos mapping function, is the initial value of the chaotic map, p is the number of items deleted in the chaotic sequence, is the average of the highlight brightness thresholds of all result matrices corresponding to the enterprise privacy data, and A represents the number of bits that need to be padded with 0 before the binary sequence formed by the enterprise privacy data.

[0104] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0105] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for encrypting and storing enterprise privacy data, characterized in that: The following steps are involved: Binary encode the enterprise privacy data to be encrypted to generate a binary sequence, segment the binary sequence according to the set number of segment bits, and perform decimal conversion on each segment of the binary sequence to obtain a decimal number equal to the number of segments; Put all decimal numbers into the two-dimensional matrix in the set order, and after all decimal numbers are put into the two-dimensional matrix, put 0 at all data point positions in the two-dimensional matrix where no data is put in, to complete the construction of the two-dimensional matrix; Calculate the classification index of each data point in the two-dimensional matrix, set the number of categories based on the values ​​of the classification indexes of all data points, the value ranges of the classification indexes under each category do not overlap, and the sum of the value ranges of the classification indexes under all categories completely covers the value ranges of the classification indexes of all data points; All data points in the two-dimensional matrix are screened according to the value range of the classification index under each category, and the corresponding result matrix under each category is obtained through the screening results of the data points; Screening the highlight points according to the distribution characteristics of the highlight points of each result matrix in the Radon parameter space, and obtaining a highlight point sequence based on the screening results; Generate a chaotic sequence through chaotic mapping, scramble the highlight point sequence with the chaotic sequence, thereby changing the position of the binary data point with a value of 1 in each result matrix, completing the scrambling of the binary data points in each result matrix, determining the corresponding position of each data point in the two-dimensional matrix according to the position of the scrambled binary data point in all result matrices, completing the scrambling of the data points in the two-dimensional matrix, converting each data point in the scrambled two-dimensional matrix into binary and splicing them, using the encryption key to obtain the encrypted ciphertext of the enterprise privacy data, and storing the obtained encrypted ciphertext; The calculation method of the classification index of each data point in the two-dimensional matrix is: Among them, C represents the classification index of the data point at the center point of the set neighborhood, norm() represents the normalization function, n represents the side length of the set neighborhood, and D represents the value of the data point at the center point in the set neighborhood. It represents the gray value of the i-th data point in the set neighborhood excluding the data point at the center point. It represents the average difference between the value of the data point at the center point in the set neighborhood and the values ​​of other data points in the set neighborhood; The method of obtaining the corresponding result matrix under each classification through the screening results of the data points is: For any classification, determine the value range of the classification index under each classification. If the classification index of a data point in the two-dimensional matrix is ​​within the value range, then the data point is retained, otherwise the data point is set to 0; Set the values ​​of all retained non-zero data points to 1, perform binarization processing, and obtain the result matrix corresponding to each classification; The method for obtaining the highlight sequence is: Perform Radon transform on each result matrix to determine the highlight points of each result matrix in the Radon parameter space, screen the highlight points of the result matrix in the Radon parameter space with the highlight point threshold corresponding to each result matrix, map the screened highlight points corresponding to all result matrices to the same Radon parameter space, and sort all the screened highlight points according to the order of their appearance to obtain a highlight point sequence; The calculation method of the highlight point threshold is: in, represents the highlight brightness threshold of the i-th result matrix, represents the number of binary data points with a value of 1 in the i-th result matrix, Represents the number of data points in the two-dimensional matrix, and W represents the empirical highlight threshold, which is set based on experience.

2. The method for encrypting and storing enterprise privacy data according to claim 1, characterized in that: Before segmenting the binary sequence according to the set number of paragraph bits, it also includes the operation of padding 0 in front of the binary sequence: Among them, A represents the number of bits that need to be padded with 0 before the binary sequence. Indicates the number of bits in the set paragraph, B indicates the overall length of the binary sequence, Represents the floor function.

3. The method for encrypting and storing enterprise privacy data according to claim 1, characterized in that: The method for generating a chaotic sequence by chaotic mapping is: by Mapping implements chaotic mapping: in, represents the value obtained after n iterations in the chaotic sequence, represents the value obtained after n-1 iterations in the chaotic sequence, is a controllable parameter; set up The number of iterations of the chaos mapping model is ,by Chaotic mapping model After iterations, each value in the obtained chaotic sequence is multiplied by v and rounded down, and the number with a rounded value of 0 is reassigned to 1 to obtain the final chaotic sequence; is the initial iteration number, and p is the number of the first items removed from the chaotic sequence.

4. The method for encrypting and storing enterprise privacy data according to claim 1, characterized in that: The method of scrambling the highlight sequence with a chaotic sequence is: The highlight point sequence is connected head to tail to form a ring sequence, and then, according to the order of position arrangement of all values ​​in the chaotic sequence, the highlight points whose sorting numbers are the same as the values ​​at each position in the chaotic sequence are extracted from the highlight point sequence in sequence, and after each extraction, the extracted and filtered highlight points are removed from the highlight point sequence to obtain a scrambled highlight point sequence; After the highlight sequence is connected end to end to form a circular sequence, the sequence number of the i-th highlight selected in the highlight sequence is , ,in, is the number of serial number cycles, For highlight sequence The number of remaining filtered highlight points in the highlight point sequence after the extraction of the filtered highlight points.

5. The method for encrypting and storing enterprise privacy data according to claim 1, characterized in that: The process of splicing the encrypted ciphertext of the enterprise privacy data includes: All data points in the scrambled two-dimensional matrix are converted from decimal to binary, and all the data points converted to binary in the scrambled two-dimensional matrix are concatenated according to the set order in which the data is placed when constructing the two-dimensional matrix to obtain the final encrypted ciphertext.

6. The method for encrypting and storing enterprise privacy data according to claim 1, characterized in that: The encryption key can be specifically divided into an encryption key and an auxiliary key, wherein the encryption key is , the subsidiary key is , is the initial value of the chaotic map; It is the mean of the highlight brightness thresholds of all result matrices corresponding to the enterprise privacy data.

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