Data processing method and device, computer equipment and readable storage medium

Abstract

The invention discloses a data processing method and device, computer equipment and a readable storage medium, and relates to the technical field of computers. The method comprises the steps that first data and second data are obtained, and the data type of the first data is different from the data type of the second data; performing first processing on the first data to obtain processed first data, and performing second processing on the second data to obtain processed second data; fusing the processed first data and the processed second data to obtain fused data; performing privacy analysis on the segmented words in the fused data, and performing out-of-order arrangement on segmented word codes corresponding to the segmented words of which the privacy values are greater than a preset value to obtain out-of-order fused data; determining an encryption public key according to the out-of-order fusion data; and encrypting the first data and the second data based on the encryption public key. By implementing the technical scheme recorded in the application, sensitive data in the data can be identified and subjected to encryption protection, leakage of sensitive information is avoided, and risks are avoided.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly to a data processing method, apparatus, computer device, and readable storage medium. Background Art

[0002] With the development and popularization of cloud computing, cloud servers have become the information service infrastructure for various business departments and enterprises. Cloud servers monitor, collect, and store information for various business departments and enterprises. However, in the prior art, cloud servers cannot analyze the monitored data. For example, they cannot combine the emotions and text data in voice data and provide protection for private data, resulting in easy data leakage and risks. Summary of the Invention

[0003] This application provides a data processing method, apparatus, computer device, and readable storage medium, which at least solves the problem of encrypting sensitive data.

[0004] In a first aspect, this application provides a data processing method, which includes:

[0005] Obtain first data and second data, where the data type of the first data is different from that of the second data;

[0006] Perform a first process on the first data to obtain the processed first data, and perform a second process on the second data to obtain the processed second data;

[0007] Fuse the processed first data and the processed second data to obtain fused data;

[0008] Perform a privacy analysis on the word segmentation in the fused data, and scramble the word segmentation codes corresponding to the word segments with a privacy value greater than a preset value to obtain scrambled fused data;

[0009] Determine an encryption public key according to the scrambled fused data;

[0010] Based on the encryption public key, encrypt the first data and the second data.

[0011] In a second aspect, this application further provides a data processing apparatus, which includes:

[0012] A data acquisition module, configured to obtain first data and second data, where the data type of the first data is different from that of the second data;

[0013] A preprocessing module, configured to perform a first process on the first data to obtain the processed first data, and perform a second process on the second data to obtain the processed second data;

[0014] A data fusion module, configured to fuse the processed first data and the processed second data to obtain fused data;

[0015] A data rearrangement module, configured to perform privacy analysis on the word segments in the fused data, and disorder the word segment codes corresponding to the word segments with privacy values greater than a preset value to obtain disordered fused data;

[0016] A public key determination module, configured to determine an encryption public key according to the disordered fused data;

[0017] A data encryption module, configured to encrypt the first data and the second data based on the encryption public key.

[0018] In a third aspect, the present application further provides a computer device, characterized by including a memory, a processor, and a data processing program stored on the memory and executable on the processor. When the processor executes the data processing program, the data processing method described in the first aspect is implemented, including:

[0019] Obtain the first data and the second data, where the data type of the first data is different from the data type of the second data;

[0020] Perform a first process on the first data to obtain the processed first data, and perform a second process on the second data to obtain the processed second data;

[0021] Fuse the processed first data and the processed second data to obtain fused data;

[0022] Perform privacy analysis on the word segments in the fused data, and disorder the word segment codes corresponding to the word segments with privacy values greater than a preset value to obtain disordered fused data;

[0023] Determine an encryption public key according to the disordered fused data;

[0024] Encrypt the first data and the second data based on the encryption public key.

[0025] In a fourth aspect, the present application further provides a computer-readable storage medium, characterized in that a data processing program is stored thereon. When the data processing program is executed by a processor, the data processing method described in the first aspect is implemented, including:

[0026] Obtain the first data and the second data, where the data type of the first data is different from the data type of the second data;

[0027] Perform a first process on the first data to obtain the processed first data, and perform a second process on the second data to obtain the processed second data;

[0028] Fuse the processed first data and the processed second data to obtain fused data;

[0029] Perform a privacy analysis on the word segmentation in the fused data, and randomly arrange the word segmentation codes corresponding to the word segments with privacy values greater than the preset value to obtain disordered fused data;

[0030] Determine the encryption public key according to the disordered fused data;

[0031] Based on the encryption public key, encrypt the first data and the second data.

[0032] In a fifth aspect, the present application also provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements the data processing method described in the first aspect, including:

[0033] Obtain the first data and the second data, where the data type of the first data is different from the data type of the second data;

[0034] Perform a first process on the first data to obtain the processed first data, and perform a second process on the second data to obtain the processed second data;

[0035] Fuse the processed first data and the processed second data to obtain fused data;

[0036] Perform a privacy analysis on the word segmentation in the fused data, and randomly arrange the word segmentation codes corresponding to the word segments with privacy values greater than the preset value to obtain disordered fused data;

[0037] Determine the encryption public key according to the disordered fused data;

[0038] Based on the encryption public key, encrypt the first data and the second data.

[0039] The beneficial effects brought by the technical solutions provided in the embodiments of the present application are: By implementing a data processing method, device, computer device, and readable storage medium provided in the embodiments of the present application, sensitive data in the data can be identified and encrypted to protect it, avoiding the leakage of sensitive information and the occurrence of risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a schematic diagram of a data processing method provided by an embodiment of the present application;

[0042] Figure 2It is a schematic diagram of a data processing device provided by an embodiment of the present application;

[0043] Figure 3 It is a schematic diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0044] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only a part rather than all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts shall fall within the protection scope of the present application.

[0045] Unless otherwise defined, the technical terms or scientific terms used in this disclosure shall have the ordinary meanings understood by those of ordinary skill in the art to which this disclosure belongs. The "first", "second", and similar terms used in this disclosure do not denote any order, quantity, or importance, but are only used to distinguish different components. Similarly, the terms such as "a", "an", or "the" do not denote a quantity limitation, but mean that there is at least one. The numbers in the accompanying drawings of the specification only represent the distinction between various functional components or modules, and do not represent the logical relationship between the components or modules. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0046] Next, various embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that in the drawings, the same reference numerals are assigned to components having substantially the same or similar structures and functions, and repeated descriptions thereof will be omitted.

[0047] Regarding the problem that sensitive information in data is prone to leakage in the prior art, the present application provides the following implementation manners.

[0048] In some embodiments, as Figure 1 shown, a data processing method, the method includes:

[0049] S100: Obtain first data and second data, where the data type of the first data is different from the data type of the second data;

[0050] S200: Perform a first process on the first data to obtain the processed first data, and perform a second process on the second data to obtain the processed second data;

[0051] S300: Fuse the processed first data and the processed second data to obtain the fused data;

[0052] S400: Perform a privacy analysis on the word segmentation in the fused data, and randomly arrange the word segmentation codes corresponding to the word segments with a privacy value greater than the preset value to obtain the randomly arranged fused data;

[0053] S500: Determine the encryption public key according to the randomly arranged fused data;

[0054] S600: Encrypt the first data and the second data based on the encryption public key.

[0055] The first data is text data. S200: Perform a first process on the first data to obtain the processed first data, specifically including:

[0056] S210a: Clean the first data to obtain the cleaned first data.

[0057] The following formula is used for data cleaning:

[0058]

[0059] where, represents the data after normalization; α represents the original data; α min represents the minimum value; α max represents the maximum value.

[0060] S220a: Perform zero-mean processing on the noise of the cleaned first data, and use the result of the zero-mean processing of the noise as the processed first data.

[0061] The following formula is used for zero-mean processing of the noise:

[0062]

[0063] where, D represents the data after zero-mean processing, τ represents the mean value; ρ represents the standard deviation. The noise data is removed through data cleaning.

[0064] The second data is voice data. S200: Perform a second process on the second data to obtain the processed second data, specifically including:

[0065] S210b: Sample the second data to obtain the sampled second data.

[0066] S220b: Perform spectral analysis on the second sampled data to obtain the frequency distribution corresponding to the second sampled data;

[0067] The calculation formula for spectral information is:

[0068]

[0069] where x(f) is the spectral information of the speech; x(n) is the speech data; f is the frequency of the speech data; N is the duration of the speech data, j represents the imaginary number, and n is an integer.

[0070] S230b: Perform noise reduction processing on the frequency distribution to obtain noise-reduced data;

[0071] S240b: Perform normalization processing on the noise-reduced data to obtain normalized noise-reduced data;

[0072] S250b: Enhance the normalized noise-reduced data to obtain the processed second data. By performing the second processing on the second data, non-speech components outside the audio range in the speech file are removed to obtain a pure speech file.

[0073] Specifically, S230b: Perform noise reduction processing on the frequency distribution to obtain noise-reduced data, including:

[0074] S231b: Consider the components with frequencies less than the first audio threshold in the frequency distribution as low-frequency non-speech components;

[0075] S232b: Consider the components with frequencies greater than the second audio threshold in the frequency distribution as high-frequency non-speech components;

[0076] S233b: Delete the low-frequency non-speech components and high-frequency non-speech components in the frequency distribution to obtain noise-reduced data. By performing the second processing on the second data, non-speech components outside the audio range in the speech file are removed to obtain a pure speech file.

[0077] Specifically, S300: Fuse the processed first data and the processed second data to obtain fused data, including:

[0078] Fuse the processed first data and the processed second data according to the following formula:

[0079]

[0080] where O represents the fused data, Δh represents the combined vector increment, represents the text vector, and the text vector is obtained by vectorizing the processed first data;

[0081] g represents the gating vector, which is determined according to the following formula:

[0082]

[0083] Among them, represents the activation function, and W x represents the gating weight matrix, and b x represents the gating bias term. represents the audio vector, which is obtained by vectorizing the processed second data;

[0084] h represents the combined vector, which is determined according to the following formula:

[0085]

[0086] Among them, tanh represents the hyperbolic tangent function, and W q represents the combined weight matrix, and b y represents the combined bias term.

[0087] S400: Perform privacy analysis on the word segmentation in the fusion data, and scramble the word segmentation codes corresponding to the word segments with privacy values greater than the preset value to obtain scrambled fusion data, including:

[0088] S410: Divide the fusion data into multiple data intervals on average;

[0089] S420: Assign values to the digits within the data intervals according to the data interval sorting;

[0090] S430: Use a random algorithm to generate random operators with the same number as the number of digits within the data intervals;

[0091] S440: Sort the digits within the data intervals according to the size of the random algorithm to obtain scrambled data.

[0092] Specifically, S500: Determine the encryption public key according to the scrambled fusion data, including:

[0093] S510: Obtain the data codes corresponding to the scrambled fusion data, and convert the data codes into quantization data;

[0094] S520: Obtain a preset number of random prime numbers;

[0095] S530: According to the preset number of random prime numbers, determine the modulus of the quantization data and the Euler function values corresponding to the preset number of random prime numbers;

[0096] The modulus of the quantization data is:

[0097] N = pq;

[0098] Among them, p and q are respectively two selected random prime numbers, and N is the modulus.

[0099] The calculation formula for the Euler's totient function value is as follows:

[0100] (q - 1)(p - 1) = T;

[0101] Where T is the Euler's totient function value.

[0102] S540: Determine the public key exponent according to the Euler's totient function value.

[0103] The encryption public key is: (E, N). Where E is the public key exponent. Correspondingly, the encryption private key is (D, N), and D is the private key exponent.

[0104] S550: Determine the encryption public key according to the public key exponent. Through the above steps, the public key for encrypting the first data and the second data, and the private key for decrypting the encryption result are obtained. For encrypting the first data and the second data, and decrypting the encrypted result.

[0105] Specifically, S600: Encrypt the first data and the second data based on the encryption public key, including:

[0106] S610: Obtain the quantization data corresponding to the shuffled and fused data;

[0107] S620: Perform a power operation on the quantization data based on the public key exponent to obtain the power operation result of the quantization data;

[0108] S630: Obtain the modulo operation result of the power operation result based on the modulus;

[0109] S640: Replace the words in the first data and the second data whose private values are greater than the preset value with the modulo operation result. Thus, the private information is encrypted to avoid the leakage of sensitive data.

[0110] It should be understood that although Figure 1 the steps in the flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in

[0111] In some other embodiments, as Figure 2 shown, a data processing device, the device includes:

[0112] A data acquisition module for acquiring first data and second data, where the data type of the first data is different from that of the second data;

[0113] A preprocessing module for performing a first process on the first data to obtain processed first data, and performing a second process on the second data to obtain processed second data;

[0114] A data fusion module for fusing the processed first data and the processed second data to obtain fused data;

[0115] A data rearrangement module for performing a privacy analysis on the word segments in the fused data, and scrambling the word segment codes corresponding to the word segments with a privacy value greater than a preset value to obtain scrambled fused data;

[0116] A public key determination module for determining an encryption public key according to the scrambled fused data;

[0117] A data encryption module for encrypting the first data and the second data based on the encryption public key.

[0118] In the preprocessing module, performing a first process on the first data to obtain processed first data includes:

[0119] Performing data cleaning on the first data to obtain cleaned first data;

[0120] Performing noise zero-mean processing on the cleaned first data, and using the result of the noise zero-mean processing as the processed first data.

[0121] In the preprocessing module, performing a second process on the second data to obtain processed second data includes:

[0122] Sampling the second data to obtain sampled second data;

[0123] Performing spectrum analysis on the sampled second data to obtain the frequency distribution corresponding to the sampled second data;

[0124] Performing noise reduction processing on the frequency distribution to obtain noise-reduced data;

[0125] Performing normalization processing on the noise-reduced data to obtain normalized noise-reduced data;

[0126] Enhancing the normalized noise-reduced data to obtain processed second data.

[0127] Among them, performing noise reduction processing on the frequency distribution to obtain noise-reduced data includes:

[0128] Regarding the components in the frequency distribution with a frequency less than the first audio threshold as low-frequency non-speech components;

[0129] In the frequency distribution, components with frequencies greater than the second audio threshold are regarded as high-frequency non-speech components;

[0130] Delete the low-frequency non-speech components and high-frequency non-speech components in the frequency distribution to obtain noise-reduced data.

[0131] In the data fusion module, fuse the processed first data and the processed second data to obtain fused data, including:

[0132] Fuse the processed first data and the processed second data according to the following formula:

[0133]

[0134] Among them, O represents the fused data, Δh represents the combined vector increment, represents the text vector, and the text vector is obtained by vectorizing the processed first data;

[0135] g represents the gating vector, which is determined according to the following formula:

[0136]

[0137] Among them, represents the activation function, W x represents the gating weight matrix, b x represents the gating bias term, represents the audio vector, and the audio vector is obtained by vectorizing the processed second data;

[0138] h represents the combined vector, which is determined according to the following formula:

[0139]

[0140] Among them, tanh represents the hyperbolic tangent function, W q represents the combining weight matrix, b y represents the combining bias term.

[0141] In the public key determination module, determine the encryption public key according to the shuffled fused data, including:

[0142] Obtain the data encoding corresponding to the shuffled fused data and convert the data encoding into quantization data;

[0143] Obtain a preset number of random prime numbers;

[0144] According to the preset number of random prime numbers, determine the modulus of the quantization data and the Euler function values corresponding to the preset number of random prime numbers;

[0145] Determine the public key exponent according to the Euler function value;

[0146] Determine the encryption public key according to the public key exponent.

[0147] In the data encryption module, encrypt the first data and the second data based on the encryption public key, including:

[0148] Obtain the quantization data corresponding to the shuffled and fused data;

[0149] Perform a power operation on the quantization data based on the public key exponent to obtain the power operation result of the quantization data;

[0150] Obtain the modulo operation result of the power operation result based on the modulus;

[0151] Replace the participles in the first data and the second data whose private values are greater than the preset value with the modulo operation result.

[0152] For the specific limitations of the data processing device described above, reference may be made to the limitations on the data processing method in the above text, which will not be elaborated here. Each module in the above data processing device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0153] In some other embodiments, as Figure 3 shown, the present application further provides a computer device, which is characterized by including a memory, a processor, and a data processing program stored on the memory and executable on the processor. When the processor executes the data processing program, the data processing method described in the first aspect is implemented, including:

[0154] S100: Obtain the first data and the second data, where the data type of the first data is different from the data type of the second data;

[0155] S200: Perform a first process on the first data to obtain the processed first data, and perform a second process on the second data to obtain the processed second data;

[0156] S210a: Perform data cleaning on the first data to obtain the cleaned first data.

[0157] S220a: Perform noise zero-mean processing on the cleaned first data, and use the result of the noise zero-mean processing as the processed first data.

[0158] The second data is voice data. S200: Perform a second process on the second data to obtain the processed second data, specifically including:

[0159] S210b: Sample the second data to obtain the sampled second data.

[0160] S220b: Perform spectral analysis on the second sampled data to obtain the frequency distribution corresponding to the second sampled data;

[0161] S230b: Perform noise reduction processing on the frequency distribution to obtain noise-reduced data;

[0162] S231b: Consider the components with frequencies less than the first audio threshold in the frequency distribution as low-frequency non-speech components;

[0163] S232b: Consider the components with frequencies greater than the second audio threshold in the frequency distribution as high-frequency non-speech components;

[0164] S233b: Delete the low-frequency non-speech components and high-frequency non-speech components in the frequency distribution to obtain noise-reduced data.

[0165] S240b: Perform normalization processing on the noise-reduced data to obtain normalized noise-reduced data;

[0166] S250b: Enhance the normalized noise-reduced data to obtain the processed second data.

[0167] S300: Fuse the processed first data and the processed second data to obtain fused data;

[0168] S400: Perform privacy analysis on the word segmentation in the fused data, and scramble the word segmentation codes corresponding to the word segments with privacy values greater than the preset value to obtain scrambled fused data;

[0169] S500: Determine the encryption public key according to the scrambled fused data;

[0170] S510: Obtain the data code corresponding to the scrambled fused data and convert the data code into quantization data;

[0171] S520: Obtain a preset number of random prime numbers;

[0172] S530: Determine the modulus of the quantization data and the Euler's totient function values corresponding to the preset number of random prime numbers according to the preset number of random prime numbers;

[0173] S540: Determine the public key exponent according to the Euler's totient function value.

[0174] S550: Determine the encryption public key according to the public key exponent.

[0175] S600: Encrypt the first data and the second data based on the encryption public key.

[0176] S610: Obtain the quantization data corresponding to the scrambled fused data;

[0177] S620: Perform a power operation on the quantized data based on the public key exponent to obtain the result of the power operation of the quantized data;

[0178] S630: Obtain the result of the modulo operation of the power operation result based on the modulus;

[0179] S640: Replace the segments in the first data and the second data where the private value is greater than the preset value with the result of the modulo operation.

[0180] In some other embodiments, a computer-readable storage medium, characterized in that a data processing program is stored thereon, and when the data processing program is executed by a processor, it implements the data processing method described in the first aspect, including:

[0181] S100: Obtain the first data and the second data, where the data type of the first data is different from the data type of the second data;

[0182] S200: Perform a first process on the first data to obtain the processed first data, and perform a second process on the second data to obtain the processed second data;

[0183] S210a: Perform data cleaning on the first data to obtain the cleaned first data.

[0184] S220a: Perform noise zero-mean processing on the cleaned first data, and use the result of the noise zero-mean processing as the processed first data.

[0185] The second data is voice data. S200: Perform a second process on the second data to obtain the processed second data, specifically including:

[0186] S210b: Sample the second data to obtain the sampled second data.

[0187] S220b: Perform spectrum analysis on the sampled second data to obtain the frequency distribution corresponding to the sampled second data;

[0188] S230b: Perform noise reduction processing on the frequency distribution to obtain the noise-reduced data;

[0189] S231b: Use the components with frequencies less than the first audio threshold in the frequency distribution as the low-frequency non-speech components;

[0190] S232b: Use the components with frequencies greater than the second audio threshold in the frequency distribution as the high-frequency non-speech components;

[0191] S233b: Delete the low-frequency non-speech components and the high-frequency non-speech components in the frequency distribution to obtain the noise-reduced data.

[0192] S240b: Perform normalization processing on the noise-reduced data to obtain the normalized noise-reduced data;

[0193] S250b: Enhance the normalized noise reduction data to obtain the processed second data.

[0194] S300: Fusion-process the processed first data and the processed second data to obtain the fusion data;

[0195] S400: Conduct a privacy analysis on the word segmentation in the fusion data, and scramble the word segmentation codes corresponding to the word segments with a privacy value greater than the preset value to obtain the scrambled fusion data;

[0196] S500: Determine the encryption public key according to the scrambled fusion data;

[0197] S510: Obtain the data code corresponding to the scrambled fusion data, and convert the data code into quantization data;

[0198] S520: Obtain a preset number of random prime numbers;

[0199] S530: According to the preset number of random prime numbers, determine the modulus of the quantization data and the Euler function values corresponding to the preset number of random prime numbers;

[0200] S540: Determine the public key exponent according to the Euler function value.

[0201] S550: Determine the encryption public key according to the public key exponent.

[0202] S600: Encrypt the first data and the second data based on the encryption public key.

[0203] S610: Obtain the quantization data corresponding to the scrambled fusion data;

[0204] S620: Based on the public key exponent, perform a power operation on the quantization data to obtain the power operation result of the quantization data;

[0205] S630: Obtain the modulo operation result of the power operation result based on the modulus;

[0206] S640: Replace the word segments with a privacy value greater than the preset value included in the first data and the second data with the modulo operation result.

[0207] In some other embodiments, a computer program product includes a computer program, characterized in that when the computer program is executed by a processor, it implements the data processing method described in the first aspect, including:

[0208] S100: Obtain the first data and the second data, wherein the data type of the first data is different from the data type of the second data;

[0209] S200: Perform a first processing on the first data to obtain the processed first data, and perform a second processing on the second data to obtain the processed second data;

[0210] S210a: Clean the first data to obtain the cleaned first data.

[0211] S220a: Perform zero-mean processing on the noise in the cleaned first data, and use the result of the zero-mean processing of the noise as the processed first data.

[0212] The second data is voice data. S200: Perform a second processing on the second data to obtain the processed second data, specifically including:

[0213] S210b: Sample the second data to obtain the sampled second data.

[0214] S220b: Perform spectrum analysis on the sampled second data to obtain the frequency distribution corresponding to the sampled second data;

[0215] S230b: Perform noise reduction processing on the frequency distribution to obtain the noise-reduced data;

[0216] S231b: Use the components with frequencies less than the first audio threshold in the frequency distribution as low-frequency non-speech components;

[0217] S232b: Use the components with frequencies greater than the second audio threshold in the frequency distribution as high-frequency non-speech components;

[0218] S233b: Delete the low-frequency non-speech components and high-frequency non-speech components in the frequency distribution to obtain the noise-reduced data.

[0219] S240b: Perform normalization processing on the noise-reduced data to obtain the normalized noise-reduced data;

[0220] S250b: Enhance the normalized noise-reduced data to obtain the processed second data.

[0221] S300: Fuse the processed first data and the processed second data to obtain the fused data;

[0222] S400: Perform privacy analysis on the word segmentation in the fused data, and scramble the word segmentation codes corresponding to the word segments with privacy values greater than the preset value to obtain the scrambled fused data;

[0223] S500: Determine the encryption public key according to the scrambled fused data;

[0224] S510: Obtain the data code corresponding to the scrambled fused data, and convert the data code into quantization data;

[0225] S520: Obtain a preset number of random prime numbers;

[0226] S530: Determine the modulus of the quantization data and the Euler's totient function values corresponding to the preset number of random prime numbers;

[0227] S540: Determine the public key exponent according to the Euler's totient function values.

[0228] S550: Determine the encryption public key according to the public key exponent.

[0229] S600: Encrypt the first data and the second data based on the encryption public key.

[0230] S610: Obtain the quantization data corresponding to the shuffled and fused data;

[0231] S620: Perform a power operation on the quantization data based on the public key exponent to obtain the power operation result of the quantization data;

[0232] S630: Obtain the modulo operation result of the power operation result based on the modulus;

[0233] S640: Replace the participles in the first data and the second data whose private values are greater than the preset value with the modulo operation result.

[0234] The beneficial effects brought by the technical solution provided by the embodiments of the present application are as follows: By implementing a data processing method, device, computer device and readable storage medium provided by the embodiments of the present application, sensitive data in the data can be identified and encrypted to avoid the leakage of sensitive information and the occurrence of risks.

[0235] Those skilled in the art can further realize that, combining the units and algorithm steps of each example described in the embodiments disclosed herein, can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0236] In particular, according to an embodiment of the present application, the processes described above with reference to the flowcharts can be implemented as steps executed under the control of a computer software program. For example, an embodiment of the present application includes a computer program product that includes a computer program loaded on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a memory, or installed from a ROM. When the computer program is executed by an external processor, the above-described functions defined in the method of the embodiment of the present application are executed.

[0237] It should be noted that the computer-readable medium of the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment of the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the embodiment of the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable signal medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0238] The above computer-readable medium may be included in the above server; or it may exist independently and not be assembled into the server. The above computer-readable medium carries one or more programs. When the one or more programs are executed by the server, the server is caused to: obtain the frame rate of an application on the terminal in response to detecting that the peripheral mode of the terminal is not activated; determine whether the user is obtaining the screen information of the terminal when the frame rate meets the screen-off condition; and control the screen to enter the immediate dimming mode in response to the determination result that the user is not obtaining the screen information of the terminal.

[0239] Computer program code for performing the operations of the embodiments of the present application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., by connecting through the Internet using an Internet service provider).

[0240] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for a system or system embodiment, since it is basically similar to a method embodiment, the description is relatively simple, and the relevant parts may refer to the partial description of the method embodiment. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative effort.

[0241] The above has introduced in detail the technical solution provided by this application. Specific examples are used herein to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, based on the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

[0242] The above has introduced in detail a data processing method, device, computer device and readable storage medium provided by this application. Specific examples are used herein to elaborate on the principle and implementation manner of this application. The above embodiments are only preferred embodiments of this application, used to help understand the method and its core idea of this application, and are not intended to limit this application. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of this application, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this application also fall within the protection scope of the claims of this application.

Claims

1. A data processing method, characterized in that, The method includes: Obtain first data and second data, where the data type of the first data is different from the data type of the second data; Perform a first process on the first data to obtain processed first data, and perform a second process on the second data to obtain processed second data; Fuse the processed first data and the processed second data to obtain fused data; Perform a privacy analysis on the word segmentation in the fused data, and randomly arrange the word segmentation codes corresponding to the word segments with a privacy value greater than a preset value to obtain randomly arranged fused data; Determine an encryption public key according to the randomly arranged fused data; Based on the encryption public key, encrypt the first data and the second data.

2. The data processing method according to claim 1, wherein The first data is text data, and the performing a first process on the first data to obtain processed first data includes: Perform data cleaning on the first data to obtain cleaned first data; Perform noise zero-mean processing on the cleaned first data, and use the result of the noise zero-mean processing as the processed first data.

3. The data processing method according to claim 1, wherein The second data is voice data, and the performing a second process on the second data to obtain processed second data includes: Sample the second data to obtain sampled second data; Perform spectrum analysis on the sampled second data to obtain the frequency distribution corresponding to the sampled second data; Perform noise reduction processing on the frequency distribution to obtain noise-reduced data; Perform normalization processing on the noise-reduced data to obtain normalized noise-reduced data; Enhance the normalized noise-reduced data to obtain the processed second data.

4. The data processing method according to claim 3, wherein The performing noise reduction processing on the frequency distribution to obtain noise-reduced data includes: Regard the components with a frequency less than a first audio threshold in the frequency distribution as low-frequency non-speech components; Regard the components with a frequency greater than a second audio threshold in the frequency distribution as high-frequency non-speech components; Delete the low-frequency non-speech components and the high-frequency non-speech components in the frequency distribution to obtain noise-reduced data.

5. The data processing method according to claim 1, wherein The fusing the processed first data and the processed second data to obtain fused data includes: Fuse the processed first data and the processed second data according to the following formula: Wherein, O represents the fused data, and Δh represents the combined vector increment. represents the text vector, which is obtained by vectorizing the processed first data. g represents a gating vector, which is determined according to the following formula: wherein, θ represents an activation function, and W x represents a gating weight matrix, and b x represents a gating bias term, represents an audio vector, and the audio vector is obtained by vectorizing the processed second data; h represents a combining vector, which is determined according to the following formula: where tanh represents the hyperbolic tangent function, W q represents the combination weight matrix, and b y represents the combination bias term.

6. The data processing method according to claim 1, wherein The determining an encryption public key according to the randomly arranged fused data includes: Obtain the data code corresponding to the randomly arranged fused data, and convert the data code into quantization data; Obtain a preset number of random prime numbers; According to the preset number of random prime numbers, determine the modulus of the quantization data and the Euler function values corresponding to the preset number of random prime numbers; Determine a public key exponent according to the Euler function value; Determine an encryption public key according to the public key exponent.

7. The data processing method according to claim 1, wherein The encrypting the first data and the second data based on the encryption public key includes: Obtain the quantization data corresponding to the randomly arranged fused data; Perform a power operation on the quantization data based on the public key exponent to obtain the power operation result of the quantization data; Obtain the modulo operation result of the power operation result based on the modulus; Replace the word segments in the first data and the second data whose private values are greater than a preset value with the modulo operation result.

8. A data processing device, characterized in that, The device includes: A data acquisition module, configured to acquire first data and second data, wherein the data type of the first data is different from the data type of the second data; A preprocessing module, configured to perform a first process on the first data to obtain processed first data, and perform a second process on the second data to obtain processed second data; A data fusion module, configured to fuse the processed first data and the processed second data to obtain fused data; A data rearrangement module, configured to perform privacy analysis on the word segments in the fused data, and disorder the word segment codes corresponding to the word segments whose private values are greater than a preset value to obtain disordered fused data; A public key determination module, configured to determine an encryption public key according to the disordered fused data; A data encryption module, configured to encrypt the first data and the second data based on the encryption public key.

9. A computer device, characterized in that, It includes a memory, a processor, and a data processing program stored on the memory and executable on the processor. When the processor executes the data processing program, it implements the data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A data processing program is stored thereon. When the data processing program is executed by a processor, it implements the data processing method according to any one of claims 1 to 7.

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