User location privacy protection method, apparatus and device, and storage medium and program product

Abstract

Disclosed in the present disclosure are a user location privacy protection method, apparatus and device, and a storage medium. Multiple data processing operations, such as standardized data processing, principal component analysis processing, noise addition and principal component analysis inverse processing, are performed on user location data in sequence, so as to obtain a principal component inverse matrix, wherein the principal component inverse matrix can effectively protect user location privacy due to the multiple data processing operations. In addition, after a final layer of encryption is performed on the principal component inverse matrix to obtain a location encryption matrix, the location encryption matrix is sent to a location based services server, so that the location based services server provides a location service after restoring the location encryption matrix, thereby improving the security of data transmission and making it difficult for attackers to acquire user location information.

Description

User location privacy protection method, device, equipment, storage medium and program product

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure is based on and claims the priority of a Chinese patent application with application number 202311633524.X and application date November 30, 2023. The entire contents of the Chinese patent application are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to the field of network security technology, and in particular to a method, apparatus, device, and storage medium for protecting user location privacy. Background Art

[0004] With the rapid development of mobile Internet technology, the location information of mobile users has become of great significance to service providers and government departments. Applications based on location services are becoming increasingly common, including road navigation, point of interest queries, information push, and so on. However, in order to obtain location services, users need to send their location information to a location-based services server (LBS). The location server returns the corresponding query content to the user based on the query request submitted by the user. During this process, the user's location information is directly exposed to the location server. If it is illegally obtained by an attacker, it will lead to the leakage of user personal information. Therefore, how to protect location privacy while sending location information and make it difficult for attackers to obtain user location information is a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The purpose of the embodiments of the present disclosure is to provide a user location privacy protection method, apparatus, device and storage medium, which can perform multiple encryption on user location data and send the encrypted user location data to a location server, thereby improving the security of data transmission and making it difficult for attackers to obtain user location information.

[0006] To achieve the above-mentioned purpose, an embodiment of the present disclosure provides a user location privacy protection method, including: performing standardized data processing on the acquired user location data to obtain a location information matrix; performing principal component analysis on the location information matrix to obtain a principal component matrix; adding the principal component matrix and the noise matrix to obtain a principal component plus noise matrix; performing principal component analysis inverse processing on the principal component plus noise matrix to obtain a principal component inverse matrix; multiplying the principal component inverse matrix with the principal component plus noise matrix to obtain a location encryption matrix; and sending the location encryption matrix to a location server.

[0007] In one embodiment, performing standardized data processing on the acquired user location data to obtain a location information matrix includes: gridding the acquired user location data to obtain a location information matrix.

[0008] In one embodiment, after obtaining the position information matrix, the method further includes: calculating the mean of each row of data in the position information matrix to obtain a mean matrix; performing data expansion on the mean matrix to obtain an extended matrix having the same dimension as the position information matrix; and performing decentralization on the extended matrix; wherein, performing principal component analysis on the position information matrix to obtain a principal component matrix includes: performing principal component analysis on the extended matrix after completing the decentralization process to obtain a principal component matrix.

[0009] In one embodiment, the performing principal component analysis on the expanded matrix after the decentralized processing to obtain the principal component matrix includes: performing principal component analysis on the expanded matrix after the decentralized processing to obtain the total amount of principal component data; extracting a plurality of principal component data from the total amount of principal component data, and using the plurality of principal component data to form the principal component matrix.

[0010] In one embodiment, after obtaining the principal component matrix, the method further includes: transposing the principal component matrix to obtain a transposed matrix of the principal component matrix; and adding noise to each position eigenvector in the transposed matrix of the principal component matrix to obtain a noise matrix.

[0011] In one embodiment, the adding of the principal component matrix and the noise matrix to obtain the principal component plus noise matrix includes: sorting the position eigenvectors in the principal component matrix to obtain a sorted principal component matrix; and adding the corresponding noise in the noise matrix to each position eigenvector in the sorted principal component matrix to obtain the principal component plus noise matrix.

[0012] In one embodiment, after obtaining the location encryption matrix, the location server restores the location encryption matrix to obtain the user location data, and provides location services based on the user location data.

[0013] To achieve the above-mentioned purpose, the embodiment of the present disclosure also provides a user location privacy protection device, including: a data standardization processing module, used to perform standardized data processing on the acquired user location data to obtain a location information matrix; a principal component analysis processing module, used to perform principal component analysis processing on the location information matrix to obtain a principal component matrix; a noise addition module, used to add the principal component matrix and the noise matrix to obtain a principal component plus noise matrix; a principal component analysis inverse processing module, used to perform principal component analysis inverse processing on the principal component plus noise matrix to obtain a principal component inverse matrix; an encryption module, used to multiply the principal component inverse matrix with the principal component plus noise matrix to obtain a location encryption matrix; and an encrypted data sending module, used to send the location encryption matrix to a location server.

[0014] To achieve the above-mentioned objectives, an embodiment of the present disclosure also provides a user location privacy protection device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the user location privacy protection method as described in any of the above-mentioned embodiments is implemented.

[0015] To achieve the above-mentioned purpose, an embodiment of the present disclosure also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the user location privacy protection method as described in any of the above embodiments.

[0016] To achieve the above objectives, an embodiment of the present disclosure further provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the user location privacy protection method described in any of the above embodiments is implemented.

[0017] Compared with the existing technology, the user location privacy protection method, device, equipment and storage medium disclosed in the present invention obtain a principal component inverse matrix after performing multiple data processing operations such as standardized data processing, principal component analysis processing, noise addition, and principal component analysis inverse processing on the user location data. This principal component inverse matrix can well protect the user's location privacy because it has undergone colorful back-cover data processing. The principal component inverse matrix is ​​encrypted in the last layer to obtain a location encryption matrix, and the location encryption matrix is ​​sent to the location server so that the location server can provide location services after restoring the location encryption matrix, which can improve the security of data transmission and make it difficult for attackers to obtain user location information. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] FIG1 is a flowchart of a method for protecting user location privacy provided by an embodiment of the present disclosure.

[0019] FIG2 is a flowchart of another method for protecting user location privacy provided by an embodiment of the present disclosure.

[0020] FIG3 is a structural block diagram of a user location privacy protection device provided by an embodiment of the present disclosure.

[0021] FIG4 is a structural block diagram of a user location privacy protection device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0023] Referring to FIG. 1 , FIG. 1 is a flowchart of a method for protecting user location privacy provided by an embodiment of the present disclosure. The method for protecting user location privacy includes the following steps S1-S6.

[0024] S1. Perform standardized data processing on the acquired user location data to obtain a location information matrix.

[0025] S2. Perform principal component analysis on the position information matrix to obtain a principal component matrix.

[0026] S3. Add the principal component matrix and the noise matrix to obtain a principal component plus noise matrix.

[0027] S4. Performing principal component analysis inverse processing on the principal component noise matrix to obtain an inverse principal component matrix.

[0028] S5. Multiply the principal component inverse matrix by the principal component noise matrix to obtain a position encryption matrix.

[0029] S6. Send the location encryption matrix to a location server.

[0030] It is worth noting that the user location privacy protection method described in the embodiment of the present disclosure is implemented by a terminal, which can be a mobile terminal such as a mobile phone or a computer.

[0031] Specifically, in step S1, performing standardized data processing on the acquired user location data to obtain a location information matrix includes: gridding the acquired user location data to obtain a location information matrix.

[0032] Exemplarily, there are many methods for obtaining user location data for mobile users. Positioning technologies such as GPS (Global Positioning System) and WiFi signals can be used, or information such as base station signal strength can be used to infer the user's location. The user location data includes geographic coordinates. The user location data is gridded to convert the geographic coordinates into matrix coordinates. Alternatively, a map of the user's location can be divided into several grids, and the matrix coordinates of the user's location can be determined based on the grid in which the user is located. The location information matrix includes several elements, each of which represents a location feature vector.

[0033] Specifically, in step S2, principal component analysis is performed on the position information matrix to obtain a principal component matrix.

[0034] Exemplarily, the position information matrix Z_0 is subjected to principal component analysis (PCA) processing, and the PCA module in the scikit-learn library in Python can be used. Principal component analysis (PCA) is to explore the degree of correlation between multiple possibly related variables, find the maximum or minimum correlation direction, and achieve the purpose of feature extraction, data compression and denoising (dimensionality reduction). The principal components of the position information matrix described in the embodiment of the present disclosure represent the main change direction of the user location data. By performing principal component analysis on the position information matrix, the direction corresponding to the maximum variance in the data can be found. These directions can be used to represent the main change trends in the position information, such as the aggregation area in the data set, the direction of the moving path, etc.

[0035] Furthermore, referring to FIG. 2 , before executing step S2 , the method further includes the following steps S11 - S13 .

[0036] S11. Calculate the mean of each row of data in the position information matrix to obtain a mean matrix.

[0037] S12. Perform data expansion on the mean matrix to obtain an expanded matrix having the same dimension as the position information matrix.

[0038] S13: Decentralize the expansion matrix.

[0039] The above step S2 specifically includes: performing principal component analysis on the expanded matrix after the decentralization process to obtain a principal component matrix.

[0040] Exemplarily, after obtaining the position information matrix Z_0, the mean is calculated row by row to obtain the mean matrix Zavg. For example, if the position information matrix Z_0 contains m rows of data, each row of data includes several elements (position eigenvectors), and the mean of each row is calculated, a mean matrix Zavg containing m elements (i.e., the mean of one row of position eigenvectors) can be obtained. The mean matrix Zavg is then expanded to obtain an expanded matrix Z0 with the same dimensions as the position information matrix Z_0. For example, a new matrix B with the same dimensions as the position information matrix Z_0 can be created, and the initial value of the new matrix B is set to the mean matrix Zavg. If the mean matrix Zavg is a scalar (i.e., a single value), then this value is assigned to each element of the new matrix B; if the mean matrix Zavg is a vector, the repeat function in the numpy library can be used to copy the vector along the specified axis so that its dimension is the same as that of the position information matrix Z_0; if the mean matrix is ​​a matrix, the tile function in the numpy library can be used to repeatedly spread the matrix to the same dimension as that of the position information matrix Z_0. Finally, the expanded matrix Z0 is decentralized, that is, the mean is subtracted from each element in the expanded matrix Z0. After obtaining the expanded matrix Z0' after the decentralized processing, the principal component analysis is performed on this expanded matrix Z0' to obtain the principal component matrix U.

[0041] Specifically, the performing principal component analysis on the expanded matrix after the decentralized processing to obtain the principal component matrix includes: performing principal component analysis on the expanded matrix after the decentralized processing to obtain the total amount of principal component data; extracting a plurality of principal component data from the total amount of principal component data, and using the plurality of principal component data to form the principal component matrix.

[0042] For example, considering that the principal component analysis process needs to be sorted according to the degree of contribution of the principal components to the overall variance, the variance corresponding to the first principal component is the largest, the variance corresponding to the second principal component is the second largest, and so on. Therefore, the first few principal components usually contain most of the variance information, while the variance contained in the following principal components is smaller, which means that the first few principal components can better represent the important features of the original position information. In the embodiment of the present disclosure, in order to remove useless data in the expanded matrix Z0', the first k principal components in the expanded matrix Z0' are extracted to obtain an n\times k principal component matrix U, that is, a principal component matrix U containing k elements, wherein the principal component matrix U has a total of n elements, each element represents a principal component, that is, n is the total amount of the principal component data. Here, n≥k, n and k are positive integers greater than 1.

[0043] Specifically, after executing step S2, the method further includes: transposing the principal component matrix to obtain a transposed matrix of the principal component matrix; and adding noise to each position eigenvector in the transposed matrix of the principal component matrix to obtain a noise matrix.

[0044] Exemplarily, the principal component matrix U is transposed to obtain a k\times n transposed matrix U^, and then the transposed matrix U^ of the principal component matrix is ​​subjected to noise processing. In the embodiment of the present disclosure, the Laplace mechanism is used to add noise to the transposed matrix U^ of the principal component matrix. The Laplace mechanism is a method of adding noise, the essence of which is to add noise to the logarithmic probability ratio of the true value, and then perform an antilogarithmic transformation to obtain the final noise result. For the k\times n transposed matrix U^, each element thereof is noised to obtain a noise matrix N.

[0045] It is worth noting that the method of adding Laplace noise to the transposed matrix U^ of the principal component matrix can refer to the existing technology, such as the existing technology of adding Laplace noise to data using Python, which will not be repeated here.

[0046] Specifically, in step S3, after obtaining the noise matrix N, the position eigenvectors in the principal component matrix U are sorted to obtain a sorted principal component matrix; each position eigenvector in the sorted principal component matrix is ​​added with the corresponding noise in the noise matrix N to obtain a principal component plus noise matrix U'.

[0047] Exemplarily, the positional eigenvectors in the principal component matrix U are sorted according to their positions after gridding to obtain a sorted principal component matrix, and corresponding Laplace noise is added to each positional eigenvector in the sorted principal component matrix. Specifically, for the positional eigenvector z_i (i.e., the positional eigenvector after averaging, data expansion, decentralization, and principal component analysis), the added noise is n_i (i.e., the noise in the noise matrix N), then the positional eigenvector after adding noise is: \tilde{z_i}=z_i+n_i.

[0048] Specifically, in step S4, the inverse processing of the principal component analysis is the process of restoring the data after the principal component analysis processing. The principal component inverse transformation process in the prior art can be referred to and will not be repeated here. In the embodiment of the present disclosure, the principal component noise matrix U' is subjected to the inverse processing of the principal component analysis to obtain the inverse principal component matrix U".

[0049] Specifically, in step S5, the principal component noise matrix U' is further encrypted. At this time, the principal component inverse matrix U" is multiplied by the principal component noise matrix U' to obtain the position encryption matrix M.

[0050] Specifically, in step S6, the location encryption matrix M is sent to the location server so that the location server can provide location services after restoring the location encryption matrix. Even if the location encryption matrix is ​​stolen, what is obtained is encrypted data after multiple data processing. It is difficult for the attacker to obtain the original location information, thereby improving the security of data transmission.

[0051] Furthermore, after obtaining the location encryption matrix M, the location server restores the location encryption matrix M to obtain the user location data, and provides location services based on the user location data.

[0052] Exemplarily, the position encryption matrix M is decrypted using the corresponding decryption key and decryption algorithm to obtain a decrypted position matrix, and the decrypted position matrix is ​​multiplied by the inverse matrix of the principal component plus noise matrix U' to obtain a restored principal component inverse matrix U". This step is equivalent to performing an inverse principal component analysis (PCA) on the principal component plus noise matrix U'. The restored principal component inverse matrix U" is inverse normalized to obtain a restored principal component plus noise matrix U', and the principal component plus noise matrix U' is subjected to denoising. For example, filtering algorithms, smoothing algorithms, and other methods can be used to remove noise to obtain a restored principal component matrix U. Since the principal component matrix U contains a large number of position eigenvectors that can characterize user location data, user location data can be obtained by analyzing this principal component matrix U.

[0053] Compared to existing technologies, the user location privacy protection method disclosed in this disclosure performs multiple data processing operations on user location data, including standardization, principal component analysis, noise addition, and inverse principal component analysis, to obtain an inverse principal component matrix. This inverse principal component matrix, due to its multiple data processing, effectively protects the user's location privacy. Furthermore, after performing a final layer of encryption on the inverse principal component matrix to obtain an encrypted location matrix, the encrypted location matrix is ​​sent to a location server, which then restores the encrypted location matrix and provides location services. This improves the security of data transmission and makes it difficult for attackers to obtain user location information.

[0054] 3 , which is a structural block diagram of a user location privacy protection device 100 provided in an embodiment of the present disclosure, the user location privacy protection device 100 includes:

[0055] The data standardization processing module 11 is used to perform standardization processing on the acquired user location data to obtain a location information matrix;

[0056] A principal component analysis processing module 12 is used to perform principal component analysis on the position information matrix to obtain a principal component matrix;

[0057] A noise adding module 13 is used to add the principal component matrix and the noise matrix to obtain a principal component plus noise matrix;

[0058] A principal component analysis inverse processing module 14 is used to perform principal component analysis inverse processing on the principal component noise matrix to obtain a principal component inverse matrix;

[0059] An encryption module 15 is configured to multiply the principal component inverse matrix by the principal component noise matrix to obtain a position encryption matrix; and

[0060] The encrypted data sending module 16 is configured to send the encrypted location matrix to a location server.

[0061] In one embodiment, the data standardization processing module 11 is specifically used to grid the acquired user location data to obtain a location information matrix.

[0062] In one embodiment, the data standardization processing module 11 is also used to: calculate the mean of each row of data in the position information matrix to obtain a mean matrix; perform data expansion on the mean matrix to obtain an extended matrix with the same dimension as the position information matrix; and perform decentralized processing on the extended matrix; the principal component analysis processing module 12 is specifically used to: perform principal component analysis processing on the extended matrix after the decentralized processing to obtain a principal component matrix.

[0063] In one embodiment, the principal component analysis processing module 12 is specifically used to: perform principal component analysis on the expanded matrix after the decentralization processing to obtain the total amount of principal component data; extract a number of principal component data from the total amount of principal component data, and form the principal component matrix with the several principal component data.

[0064] In one embodiment, the user location privacy protection device 100 further includes: a noise matrix generation module, which is used to transpose the principal component matrix to obtain a transposed matrix of the principal component matrix; and add noise to each position eigenvector in the transposed matrix of the principal component matrix to obtain a noise matrix.

[0065] In one embodiment, the noise addition module 13 is specifically used to: sort the position eigenvectors in the principal component matrix to obtain a sorted principal component matrix; add the corresponding noise in the noise matrix to each position eigenvector in the sorted principal component matrix to obtain a principal component noise matrix.

[0066] In one embodiment, after obtaining the location encryption matrix, the location server restores the location encryption matrix to obtain the user location data, and provides location services based on the user location data.

[0067] It is worth noting that the working process of each module in the user location privacy protection device 100 described in the embodiment of the present disclosure can refer to the working process of the user location privacy protection method described in the above embodiment, and will not be repeated here.

[0068] Compared to existing technologies, the user location privacy protection device 100 disclosed herein performs multiple data processing operations on user location data, including standardization, principal component analysis, noise addition, and inverse principal component analysis, to obtain an inverse principal component matrix. This inverse principal component matrix, due to its multiple data processing, effectively protects the user's location privacy. Furthermore, after performing a final layer of encryption on the inverse principal component matrix to obtain an encrypted location matrix, the encrypted location matrix is ​​sent to a location server, which then restores the encrypted location matrix and provides location services. This improves data transmission security and makes it difficult for attackers to obtain user location information.

[0069] Referring to FIG. 4 , FIG. 4 is a block diagram of a user location privacy protection device 200 provided in an embodiment of the present disclosure. The user location privacy protection device 200 includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps of the aforementioned user location privacy protection method embodiments, such as steps S1 to S6, are implemented.

[0070] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to implement the present disclosure. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the user location privacy protection device 200.

[0071] The user location privacy protection device 200 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of the user location privacy protection device 200 and does not limit the user location privacy protection device 200. The user location privacy protection device 200 may include more or fewer components than shown in the diagram, or may combine certain components or different components. For example, the user location privacy protection device 200 may also include input and output devices, network access devices, buses, and the like.

[0072] The processor 21 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor 21 is the control center of the user location privacy protection device 200, connecting various parts of the user location privacy protection device 200 using various interfaces and lines.

[0073] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements the various functions of the user location privacy protection device 200 by running or executing the computer programs and / or modules stored in the memory 22 and accessing the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 22 may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0074] In particular, if the modules / units integrated in the user location privacy protection device 200 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, it can implement the steps of the above-mentioned various method embodiments. In particular, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. In some embodiments, the computer-readable storage medium can be a non-transitory computer-readable storage medium.

[0075] In addition, the technical solutions described in the embodiments of the present disclosure may be arbitrarily combined without conflict, and the technical solutions after such arbitrary combination fall within the protection scope described in the present disclosure.

[0076] The above is a preferred embodiment of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present disclosure. These improvements and modifications are also considered to be within the scope of protection of the present disclosure.

Claims

1. A method for protecting user location privacy, comprising: Performing standardized data processing on the acquired user location data to obtain a location information matrix; Performing principal component analysis on the position information matrix to obtain a principal component matrix; Adding the principal component matrix and the noise matrix to obtain a principal component plus noise matrix; Performing inverse principal component analysis processing on the principal component plus noise matrix to obtain an inverse principal component matrix; Multiplying the principal component inverse matrix by the principal component plus noise matrix to obtain a position encryption matrix; and The location encryption matrix is ​​sent to a location server.

2. The method of claim 1, wherein: The obtained user location data is subjected to standardized data processing to obtain a location information matrix, including: The acquired user location data is gridded to obtain a location information matrix.

3. The method of claim 1, wherein: After obtaining the position information matrix, the method further includes: Calculate the mean of each row of data in the position information matrix to obtain a mean matrix; Performing data expansion on the mean matrix to obtain an extended matrix having the same dimension as the position information matrix; and Decentralizing the expansion matrix; The performing of principal component analysis on the position information matrix to obtain a principal component matrix includes: The expanded matrix after the decentralization process is subjected to principal component analysis to obtain a principal component matrix.

4. The method of claim 3, wherein: The principal component analysis is performed on the expanded matrix after the decentralization process to obtain a principal component matrix, including: Performing principal component analysis on the expanded matrix after the decentralization process to obtain the total amount of principal component data; and A plurality of principal component data are extracted from the total amount of principal component data, and the principal component matrix is ​​formed using the plurality of principal component data.

5. The method of claim 1, wherein: After obtaining the principal component matrix, the method further includes: Transposing the principal component matrix to obtain a transposed matrix of the principal component matrix; and Noise is added to each position eigenvector in the transposed matrix of the principal component matrix to obtain the noise matrix.

6. The method of claim 1, wherein: The step of adding the principal component matrix and the noise matrix to obtain a principal component plus noise matrix comprises: Sorting the positional eigenvectors in the principal component matrix to obtain a sorted principal component matrix; and The noise in the corresponding noise matrix is ​​added to each position eigenvector in the sorted principal component matrix to obtain the principal component plus noise matrix.

7. The method of claim 1, wherein: After obtaining the location encryption matrix, the location server restores the location encryption matrix to obtain the user location data, and provides location services based on the user location data.

8. A user location privacy protection device, comprising: A data standardization processing module is used to perform standardized data processing on the acquired user location data to obtain a location information matrix; A principal component analysis processing module is used to perform principal component analysis on the position information matrix to obtain a principal component matrix; A noise adding module, used for adding the principal component matrix and the noise matrix to obtain a principal component plus noise matrix; A principal component analysis inverse processing module is used to perform principal component analysis inverse processing on the principal component plus noise matrix to obtain a principal component inverse matrix; an encryption module, used for multiplying the inverse principal component matrix by the principal component plus noise matrix to obtain a position encryption matrix; and The encrypted data sending module is used to send the encrypted position matrix to a position server.

9. A user location privacy protection device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, the computer-readable storage medium comprising a stored computer program, wherein: When the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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