CN116484430A - Encryption protection method for user privacy data of intelligent psychological platform - Google Patents
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Abstract
The invention relates to the technical field of data security processing, in particular to a user privacy data encryption protection method for an intelligent psychological platform. The method obtains an initial influence matrix through a user head portrait, and utilizes a variable convolution kernel which contains user specific data and has strong variability to process the initial influence matrix to obtain a final influence matrix. The final influence matrix is a matrix specific to each user and has stronger confusion, so that a key influence factor is obtained based on the final influence matrix, the privacy psychological data is encrypted based on the key influence factor, the platform public key and the user private key, and the public visible data is encrypted by using the platform public key to obtain encrypted data. According to the invention, through targeted encryption of the public visible data and the private psychological data, the specificity between the data is improved, the encryption relevance is reduced, the encryption effect of the encrypted data is better, and the protection capability of the intelligent psychological platform on the data is improved.
Description
Technical Field
The invention relates to the technical field of data security processing, in particular to a user privacy data encryption protection method for an intelligent psychological platform.
Background
The intelligent psychological platform is mainly used for storing psychological health detection data, psychological dispersion results and process data provided by student users in the psychological consultation process, namely a large amount of information with extremely high privacy of the student users is stored in the intelligent psychological platform. The information with extremely high privacy needs to be protected with emphasis, so that the private data of the student user needs to be encrypted with emphasis in the intelligent psychological platform.
In the prior art, the smart psychological platform generally encrypts private information by adopting a strong encryption algorithm, encrypts all information of a user by adopting the same key set, and comprises public visible information and private information in the information of the user, the encryption association degree of the public visible information and the private information is higher, no specialization exists between the information, and if an attacker decodes the public visible information, the decoding result can influence private data, so that the data is leaked.
Disclosure of Invention
In order to solve the technical problem that user data is easy to leak due to high association degree between user encrypted data in a smart psychological platform without specialization, the invention aims to provide a smart psychological platform user privacy data encryption protection method, which adopts the following specific technical scheme:
the invention provides a method for protecting privacy data encryption of a user of an intelligent psychological platform, which comprises the following steps:
acquiring user data and a platform public key of a target user in an intelligent psychological platform, wherein the user data comprises a user private key, a user head portrait, public visible data and private psychological data;
obtaining a binary initial influence matrix according to pixel values in the user head portrait; converting characters in the privacy psychological data into sacii codes and sequentially carrying out logic operation to obtain adjustment data codes; constructing a variable convolution kernel and carrying out convolution processing on the initial influence matrix to obtain a convolution influence matrix; continuing to carry out convolution processing on the convolution influence matrix to obtain a new convolution influence matrix until the confusion degree of the convolution influence matrix meets a preset standard, and taking the latest convolution influence matrix as a final influence matrix; the central data of the variable convolution kernel is obtained according to the data of the corresponding position in a matrix to be convolved and the length of the privacy psychological data, the non-central data is composed of the adjustment data codes, and the matrix to be convolved is the initial influence matrix or the convolution influence matrix;
arranging and integrating the data in the final influence matrix to obtain a key influence factor; encrypting the privacy psychological data by the key influence factor, the platform public key and the user private key, and encrypting the public visible data by the platform public key to obtain encrypted data.
Further, the obtaining a binary initial impact matrix according to the pixel values in the user head portrait includes:
obtaining an image data matrix of the user head portrait; compressing the image data matrix to a preset target size to obtain a compressed image data matrix; and carrying out threshold segmentation on the element data in the image data matrix through an Ojin threshold algorithm to obtain a binarization matrix, wherein the element data in the binarization matrix comprises 0 and 1 data, and the binarization matrix is used as the initial influence matrix.
Further, the method for acquiring the adjustment data code comprises the following steps:
and sequentially performing exclusive OR logic operation on the sacii codes to obtain the adjustment data codes.
Further, the method for obtaining the center data of the variable convolution kernel comprises the following steps:
if the length of the privacy psychological data is an odd number, performing OR logic operation on binary number 1 and data of the corresponding position of the central data of the variable convolution kernel in the matrix to obtain the central data of the variable convolution kernel;
and if the length of the privacy psychological data is even, performing OR logic operation on binary 0 and the data of the corresponding position of the central data of the variable convolution kernel in the matrix to obtain the central data of the variable convolution kernel.
Further, the method for obtaining the confusion degree of the convolution influence matrix comprises the following steps:
acquiring the rank of the convolution influence matrix and the information entropy of data in the matrix; and obtaining the confusion degree according to the rank and the information entropy, wherein the rank and the information entropy are in positive correlation with the confusion degree.
Further, the method for acquiring the key influence factor comprises the following steps:
splitting the final influence matrix according to rows to obtain a row sequence; and sequentially performing an AND logic operation on the row sequence to obtain the key influence factor.
Further, the encryption method of the encrypted data comprises the following steps:
using the key influence factor, the platform public key and the user private key as replacement keys, and encrypting the privacy psychological data by using a DES encryption algorithm;
and using the platform public key as a replacement key, and encrypting the public visible data by using a DES encryption algorithm.
The invention has the following beneficial effects:
the invention divides the user data into public visible data and privacy psychological data; the public key of the platform is directly used for encrypting the public visible data, so that the computing power of the platform is saved. The initial influence matrix is further built by combining the user head portrait, and the user head portrait is non-valuable information which can be edited and modified by a user at will and has the characteristic of updating and modifying at random, so that the subsequent encrypted data can be updated along with the modification of the user head portrait according to the initial influence matrix built by the user head portrait, and the safety of the data is improved. In order to further enable the initial influence matrix to have an irregular and chaotic effect, the initial influence matrix is subjected to convolution processing, and the used convolution kernel is a variable convolution kernel, so that the irregularity of the initial influence matrix is further improved, and the safety of subsequent encrypted data is improved. The variable convolution kernel is formed based on the privacy psychological data of the user and the element data of the corresponding position of the matrix, so that the variable convolution kernel among different users is different, the variable convolution kernel has specificity, the obtained final influence matrix has specificity, the key influence factor with user specific information is further obtained, and the platform public key and the user private key are further combined to realize encryption of the psychological data of the user factors to be encrypted. According to the invention, the public visible data and the private psychological data are respectively encrypted, so that the specificity between the public visible data and the private psychological data is improved, and the encryption relevance between the encrypted data is reduced; for the privacy psychological data, by constructing the key influence factors with the user characteristics, the association degree of the encryption characteristics among the user data is reduced, and the specificity encryption of the privacy psychological data is realized. The invention improves the safety of the whole user information and avoids the leakage of the user data.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a method for protecting privacy data of a user of a smart psychological platform according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of a user privacy data encryption protection method of a smart psychological platform according to the invention, which is provided by combining the accompanying drawings and the preferred embodiment, wherein the detailed description is as follows. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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 invention belongs.
The following specifically describes a specific scheme of the user privacy data encryption protection method for the intelligent psychological platform provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for protecting privacy data encryption of a smart psychological platform user according to an embodiment of the present invention is shown, where the method includes:
step S1: user data and a platform public key of a target user in the intelligent psychological platform are obtained.
In the smart psychological platform, user data is mainly composed of basic information data and psychological information data of a user, such as an account head, a name, an age, a date of birth, an identification card number, a submitted psychological problem record, a user inquiry record and the like of the user. In order to enhance the security of the subsequent encrypted data, public visible data and private psychological data in the user data need to be distinguished, and targeted encryption is performed in each subsequent encryption process.
It should be noted that, in the smart psychological platform, in order to encrypt the user data, the platform public key of the platform exists in the platform, and when the user exists an account in the platform, the independent user private key of the user is generated based on the user account. The platform public key and the user private key are both important data used in encrypting the data.
In order to further improve the safety of the subsequent encrypted data, the embodiment of the invention increases the specificity of the encrypted data among users and also extracts the user head portrait in the user data. Because the user head portrait is freely editable information for the user, although the user head portrait does not have any information value, the user head portrait has the characteristic of irregular updating and modification, so that the user head portrait information is used as an encryption basis, and after the user modifies the head portrait, the encryption of the whole privacy psychological data is updated, so that the security of the encrypted data is further improved.
Step S2: obtaining a binary initial influence matrix according to pixel values in the user head portrait; converting characters in the privacy psychological data into sacii codes and sequentially carrying out logic operation to obtain adjustment data codes; constructing a variable convolution kernel and carrying out convolution processing on the initial influence matrix to obtain a convolution influence matrix; the central data of the variable convolution kernel is obtained according to the length of the data of the corresponding position in the matrix to be convolved and the privacy psychological data, and the non-central data is composed of adjustment data codes; and continuing to carry out convolution processing on the convolution influence matrix to obtain a new convolution influence matrix until the confusion degree of the convolution influence matrix meets a preset standard, and taking the latest convolution influence matrix as a final influence matrix.
Based on the characteristic that the user head portrait has random modification at an irregular period, a binary initial influence matrix is obtained according to pixel values in the user head portrait, namely, the initial influence matrix only comprises binary data of 0 and 1.
Preferably, the method for acquiring the initial influence matrix includes:
obtaining an image data matrix of a user head portrait; compressing the image data matrix to a preset target size to obtain a compressed image data matrix; and carrying out threshold segmentation on the element data in the image data matrix through an Ojin threshold algorithm to obtain a binarization matrix, wherein the element data in the binarization matrix comprises 0 data and 1 data, and the binarization matrix is used as an initial influence matrix. In one embodiment of the invention, in a compressed image data matrix, an optimal segmentation threshold value is obtained by using an Ojin threshold algorithm, the data value of element data which is larger than the optimal segmentation threshold value is replaced by 1, and the data value of element data which is not larger than the optimal segmentation threshold value is replaced by 0, so as to obtain a binarization matrix; for convenience of subsequent processing, the target size is set to 64×64, i.e., the initial influence matrix is a matrix of 64 rows and 64 columns.
Because the initial influence matrix is obtained based on the user head portrait, the correlation of the internal information is strong, and the initial influence matrix is required to be processed to improve the safety and the encryption effect of the subsequent encryption process, so that the correlation of the internal information is reduced, the confusion of the information is increased, and an attacker is difficult to crack.
In order to enhance the information complexity of the initial influence matrix, convolution processing is needed to be carried out on the initial influence matrix, a variable convolution kernel is constructed, the whole initial influence matrix is traversed, the information in the initial influence matrix is processed, and the information complexity is enhanced. In order to further improve the specificity of the influencing matrix, the central data in the variable convolution kernel is obtained according to the data of the corresponding position in the matrix to be convolved and the length of the privacy psychological data, and the non-central data is composed of the adjustment data codes. The data code is adjusted based on the character sacii code in the privacy psychological data, so that the variable convolution kernels of each user are different, and the variable convolution kernels corresponding to different positions in the matrix to be convolved are possibly different, so that the specificity of the variable convolution kernels is greatly enhanced. The matrix to be convolved is an initial influence matrix or a convolved influence matrix.
Preferably, the method for acquiring the adjustment data code includes:
and sequentially performing exclusive OR logic operation on the sacii codes to obtain the adjustment data codes. Expressed by the formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,for adjusting the data code->Sacii code for the first character in the private psychological data,/a->Sacii code for the second character in the private psychological data, < >>For privacy psychological data +.>Sacii code of individual characters, +.>Character length for privacy psychological data, +.>Is an exclusive or logical operator.
It should be noted that, the sacii code is converted into a technical means well known to those skilled in the art, the sacii code is a fixed 8-bit binary code, that is, the sacii code of each character in the privacy psychological data is 8 bits, and after the serial exclusive or operation, the adjustment data code is also an 8-bit binary code.
Because the non-central data of the variable convolution kernel is composed of the adjustment data codes, in one embodiment of the present invention, the adjustment data codes are sequentially arranged according to element positions to obtain the variable convolution kernel. Because the adjustment data code is a binary code of 8, the variable convolution kernel is a convolution kernel of 3×3 size, specifically expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,for the variable convolution kernel +.>To->To adjust 1 st element to 8 th element in the data code, < >>Is the central data of the variable convolution kernel.
It should be noted that, in other embodiments, other arrangements may be used to arrange the elements in the adjustment data code, which is not limited herein.
Preferably, the method for acquiring center data of the variable convolution kernel includes:
if the length of the privacy psychological data is an odd number, performing OR logic operation on binary number 1 and the data of the central data of the variable convolution kernel at the corresponding position in the matrix to be convolved to obtain the central data of the variable convolution kernel; if the length of the privacy psychological data is even, performing OR logic operation on binary 0 and the data of the corresponding position of the central data of the variable convolution kernel in the matrix to be convolved, and obtaining the central data of the variable convolution kernel.
By constructing the variable convolution kernels, the variable convolution kernels of the variable convolution kernels are arranged among different users, and because the central data of the variable convolution kernels are obtained by the matrix to be convolved and the length of the privacy psychological data, the variable convolution kernels at different positions on the matrix to be convolved are different. Therefore, the initial influence matrix is subjected to convolution processing through the variable convolution check with strong variability and strong specificity, the confusion of data in the matrix can be enhanced, the convolution influence matrix is obtained, the convolution processing is continued in the convolution influence matrix, a new convolution influence matrix can be obtained, the convolution process can be stopped until the confusion of the convolution influence matrix meets the preset standard, and the latest obtained convolution influence matrix is used as the final influence matrix. It should be noted that the convolution processing is a technical means well known to those skilled in the art, and will not be described herein.
Preferably, the method for obtaining the confusion degree of the convolution influence matrix in one embodiment of the invention comprises the following steps:
acquiring the rank of a convolution influence matrix and the information entropy of data in the matrix; and obtaining the confusion degree according to the rank and information entropy, wherein the rank and the information entropy are in positive correlation with the confusion degree.
In one embodiment of the invention, the formula for the degree of confusion is:
wherein, the liquid crystal display device comprises a liquid crystal display device,for confusion degree (I)>For rank (S)>For information entropy->For the convolution influence matrix the element value is +.>Probability of time correspondence->For regulating the coefficient->Is to avoid->A value of 0 results in meaningless, in one embodiment of the invention, a +>Set to 0.1 @, @>Is a natural constant. In the confusion formula, the mapping and opening operations of the denominator 128 and the information entropy through the exponential function based on the natural constant are used for adjusting the value range of the confusion, and finally the value range of the confusion is limited between 0 and 1.
It should be noted that, in other embodiments, the rank and the information entropy may be processed in the form of other basic mathematical operations or function mapping, so that the rank and the information entropy are in positive correlation with the degree of confusion, and such operations are technical means well known to those skilled in the art, and are not described herein.
In one embodiment of the invention, a confusion is considered to be standard when the confusion reaches 0.8.
Step S3: arranging and integrating the data in the final influence matrix to obtain a key influence factor; encrypting the privacy psychological data by the key influencing factor, the platform public key and the user private key, and encrypting the public visible data by the platform public key to obtain encrypted data.
The final influence matrix is in a matrix form, the size of the final influence matrix is the same as that of the initial influence matrix, and in order to facilitate subsequent encryption, data in the final influence matrix need to be arranged and integrated to obtain a key influence factor.
Preferably, in one embodiment of the present invention, the method for acquiring the key influence factor includes:
splitting the final influence matrix according to rows to obtain a row sequence; and carrying out phase-to-phase logic operation on the row sequence in turn to obtain the key influence factor. It should be noted that, because the final impact matrix is a 64×64 matrix in one embodiment of the present invention, after splitting, 64 row sequences with a length of 64 may be obtained, and after phase-separating the row sequences in sequence, a 64-bit key impact factor may be obtained.
Because the privacy psychological data is required to be encrypted, the privacy psychological data is encrypted by the key influencing factors, the platform public key and the user private key, the privacy psychological data is encrypted in a targeted mode, the public visible data is further encrypted by the platform public key, all data of the user are encrypted, and encrypted data are obtained. In the encrypted data, the encryption result of the privacy psychological data has smaller relevance with the encryption result of the public visible data, has stronger specificity, and the encryption results among different users also have specificity, so that the obtained encrypted data has excellent encryption protection function, and the data security of the intelligent psychological platform is higher.
Preferably, the encryption method for adding data in one embodiment of the present invention includes:
using the key influence factor, the platform public key and the user private key as replacement keys, and encrypting the privacy psychological data by using a DES encryption algorithm;
and using the platform public key as a replacement key, and encrypting the public visible data by using a DES encryption algorithm.
It should be noted that, the DES encryption algorithm is a technical means well known to those skilled in the art, details of the specific algorithm are not described again, and only basic processing steps of the DES encryption algorithm in one embodiment of the present invention are briefly described here:
(1) 64 bits of plaintext data are input and an initial permutation IP is performed based on the key set. If the plaintext is the private psychological data, the key group consists of a key influencing factor, a platform public key and a user private key; if the plaintext is public visible data, the key set consists of a platform public key.
(2) After the initial permutation IP, the plaintext data is divided into left and right parts, each of which is 32 bits.
(3) And carrying out 16 rounds of operation under the control of the key group.
(4) After 16 rounds, the left and right parts are exchanged and connected together, and then reverse replacement is carried out.
(5) And outputting 64-bit ciphertext.
Finally, the encrypted data containing the public ciphertext and the private ciphertext can be stored in the intelligent psychological platform, so that the follow-up platform can conveniently take the encrypted data.
In summary, the embodiment of the invention obtains the initial influence matrix through the user head portrait, and processes the initial influence matrix by using the variable convolution kernel which contains the user specific data and has strong variability to obtain the final influence matrix. The final influence matrix is a matrix specific to each user and has stronger confusion, so that a key influence factor is obtained based on the final influence matrix, the privacy psychological data is encrypted based on the key influence factor, the platform public key and the user private key, and the public visible data is encrypted by using the platform public key to obtain encrypted data. According to the invention, through targeted encryption of the public visible data and the private psychological data, the specificity between the data is improved, the encryption relevance is reduced, the encryption effect of the encrypted data is better, and the protection capability of the intelligent psychological platform on the data is improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (7)
1. The method for protecting the privacy data encryption of the user of the intelligent psychological platform is characterized by comprising the following steps:
acquiring user data and a platform public key of a target user in an intelligent psychological platform, wherein the user data comprises a user private key, a user head portrait, public visible data and private psychological data;
obtaining a binary initial influence matrix according to pixel values in the user head portrait; converting characters in the privacy psychological data into sacii codes and sequentially carrying out logic operation to obtain adjustment data codes; constructing a variable convolution kernel and carrying out convolution processing on the initial influence matrix to obtain a convolution influence matrix; continuing to carry out convolution processing on the convolution influence matrix to obtain a new convolution influence matrix until the confusion degree of the convolution influence matrix meets a preset standard, and taking the latest convolution influence matrix as a final influence matrix; the central data of the variable convolution kernel is obtained according to the data of the corresponding position in a matrix to be convolved and the length of the privacy psychological data, the non-central data is composed of the adjustment data codes, and the matrix to be convolved is the initial influence matrix or the convolution influence matrix;
arranging and integrating the data in the final influence matrix to obtain a key influence factor; encrypting the privacy psychological data by the key influence factor, the platform public key and the user private key, and encrypting the public visible data by the platform public key to obtain encrypted data.
2. The method for protecting privacy data encryption of intelligent psychological platform users according to claim 1, wherein the obtaining the binary initial impact matrix according to the pixel values in the user head portrait comprises:
obtaining an image data matrix of the user head portrait; compressing the image data matrix to a preset target size to obtain a compressed image data matrix; and carrying out threshold segmentation on the element data in the image data matrix through an Ojin threshold algorithm to obtain a binarization matrix, wherein the element data in the binarization matrix comprises 0 and 1 data, and the binarization matrix is used as the initial influence matrix.
3. The method for protecting privacy data encryption of intelligent psychological platform users according to claim 1, wherein the method for acquiring the adjustment data code comprises the following steps:
and sequentially performing exclusive OR logic operation on the sacii codes to obtain the adjustment data codes.
4. The smart psychological platform user privacy data encryption protection method according to claim 1, wherein the central data acquisition method of the variable convolution kernel comprises:
if the length of the privacy psychological data is an odd number, performing OR logic operation on binary number 1 and data of the corresponding position of the central data of the variable convolution kernel in the matrix to obtain the central data of the variable convolution kernel;
and if the length of the privacy psychological data is even, performing OR logic operation on binary 0 and the data of the corresponding position of the central data of the variable convolution kernel in the matrix to obtain the central data of the variable convolution kernel.
5. The method for protecting privacy data encryption of users of intelligent psychological platform according to claim 1, wherein the method for obtaining confusion of the convolution influence matrix comprises the following steps:
acquiring the rank of the convolution influence matrix and the information entropy of data in the matrix; and obtaining the confusion degree according to the rank and the information entropy, wherein the rank and the information entropy are in positive correlation with the confusion degree.
6. The method for protecting privacy data encryption of users of intelligent psychological platform according to claim 1, wherein the method for obtaining the key influencing factors comprises the following steps:
splitting the final influence matrix according to rows to obtain a row sequence; and sequentially performing an AND logic operation on the row sequence to obtain the key influence factor.
7. The smart psychological platform user privacy data encryption protection method according to claim 1, wherein the encryption method of the encrypted data comprises the following steps:
using the key influence factor, the platform public key and the user private key as replacement keys, and encrypting the privacy psychological data by using a DES encryption algorithm;
and using the platform public key as a replacement key, and encrypting the public visible data by using a DES encryption algorithm.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110598438A (en) * | 2019-07-19 | 2019-12-20 | 福州大学 | In-cloud protection outsourcing data privacy protection system based on deep convolutional neural network |
| CN111737706A (en) * | 2020-05-11 | 2020-10-02 | 华南理工大学 | Front-end portrait encryption and identification method with biological feature privacy protection function |
| CN112016120A (en) * | 2020-08-26 | 2020-12-01 | 支付宝(杭州)信息技术有限公司 | Event prediction method and device based on user privacy protection |
| CN112347495A (en) * | 2020-11-15 | 2021-02-09 | 北京物资学院 | Trusted privacy intelligent service computing system and method based on block chain |
| CN112528064A (en) * | 2020-12-10 | 2021-03-19 | 西安电子科技大学 | Privacy-protecting encrypted image retrieval method and system |
| CN113194334A (en) * | 2021-04-16 | 2021-07-30 | 厦门智瞳科技有限公司 | Image processing method and device for protecting privacy, terminal and storage medium |
| WO2021190424A1 (en) * | 2020-03-25 | 2021-09-30 | 支付宝(杭州)信息技术有限公司 | Method and apparatus for carrying out multi-party joint dimension reduction processing on private data |
| CN113920323A (en) * | 2021-11-18 | 2022-01-11 | 西安电子科技大学 | Different-chaos hyperspectral image classification method based on semantic graph attention network |
| CN115242475A (en) * | 2022-07-14 | 2022-10-25 | 南京化科天创科技有限公司 | Big data secure transmission method and system |
| CN115659387A (en) * | 2022-09-27 | 2023-01-31 | 浙江工业大学 | Neural-channel-based user privacy protection method, electronic device and medium |
| CN115941351A (en) * | 2022-12-29 | 2023-04-07 | 国网浙江省电力有限公司桐乡市供电公司 | Trusted privacy computing system based on cloud service and encryption technology |
-
2023
- 2023-06-21 CN CN202310735396.3A patent/CN116484430B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210019428A1 (en) * | 2019-07-19 | 2021-01-21 | Fuzhou University | Preservation system for preserving privacy of outsourced data in cloud based on deep convolutional neural network |
| CN110598438A (en) * | 2019-07-19 | 2019-12-20 | 福州大学 | In-cloud protection outsourcing data privacy protection system based on deep convolutional neural network |
| WO2021190424A1 (en) * | 2020-03-25 | 2021-09-30 | 支付宝(杭州)信息技术有限公司 | Method and apparatus for carrying out multi-party joint dimension reduction processing on private data |
| WO2021227349A1 (en) * | 2020-05-11 | 2021-11-18 | 华南理工大学 | Front-end facial image encryption and recognition method for biometric privacy protection |
| CN111737706A (en) * | 2020-05-11 | 2020-10-02 | 华南理工大学 | Front-end portrait encryption and identification method with biological feature privacy protection function |
| CN112016120A (en) * | 2020-08-26 | 2020-12-01 | 支付宝(杭州)信息技术有限公司 | Event prediction method and device based on user privacy protection |
| CN112347495A (en) * | 2020-11-15 | 2021-02-09 | 北京物资学院 | Trusted privacy intelligent service computing system and method based on block chain |
| CN112528064A (en) * | 2020-12-10 | 2021-03-19 | 西安电子科技大学 | Privacy-protecting encrypted image retrieval method and system |
| CN113194334A (en) * | 2021-04-16 | 2021-07-30 | 厦门智瞳科技有限公司 | Image processing method and device for protecting privacy, terminal and storage medium |
| CN113920323A (en) * | 2021-11-18 | 2022-01-11 | 西安电子科技大学 | Different-chaos hyperspectral image classification method based on semantic graph attention network |
| CN115242475A (en) * | 2022-07-14 | 2022-10-25 | 南京化科天创科技有限公司 | Big data secure transmission method and system |
| CN115659387A (en) * | 2022-09-27 | 2023-01-31 | 浙江工业大学 | Neural-channel-based user privacy protection method, electronic device and medium |
| CN115941351A (en) * | 2022-12-29 | 2023-04-07 | 国网浙江省电力有限公司桐乡市供电公司 | Trusted privacy computing system based on cloud service and encryption technology |
Non-Patent Citations (3)
| Title |
|---|
| XINGYUAN WANG; PENGBO LIU: "A New Full Chaos Coupled Mapping Lattice and Its Application in Privacy Image Encryption", IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I: REGULAR PAPERS, pages 1291 * |
| 张洁: "移动云计算环境下新的数据隐私保护算法研究", 科技通报, pages 227 - 230 * |
| 王乐;杨哲荣;刘容京;王祥;: "基于属性加密算法的可穿戴设备系统隐私保护方法研究", 信息网络安全, no. 06, pages 83 - 90 * |
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