CN113868690B - Trusted deposit certificate based privacy calculation method and system - Google Patents

Trusted deposit certificate based privacy calculation method and system Download PDF

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Publication number
CN113868690B
CN113868690B CN202111456878.2A CN202111456878A CN113868690B CN 113868690 B CN113868690 B CN 113868690B CN 202111456878 A CN202111456878 A CN 202111456878A CN 113868690 B CN113868690 B CN 113868690B
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pixel
image
face image
value
face
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CN113868690A (en
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邹耀增
詹蕴学
刘熙
韩声利
刘文用
戴燎元
李鹏
李程艳
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Hunan Fenghui Yinjia Science And Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes

Abstract

The invention relates to the technical field of privacy calculation, and discloses a privacy calculation method based on trusted certificate storage, which comprises the following steps: acquiring a face image, carrying out gray processing on the face image, and carrying out binarization processing on the face gray image to obtain a normalized face image; generating a chaos sequence, and performing pixel-level chaos processing on the normalized human face image by using the chaos sequence; generating an encryption initial parameter, and encrypting the chaotically processed image by utilizing a nonlinear equation set image replacement algorithm; and when decryption is required, performing decryption processing according to the encrypted initial parameters and performing chaotic anti-confusion processing on the decrypted image. The invention conducts pixel confusion on the pixels of the face image by utilizing the chaotic sequence, so that the pixel value distribution of the face is uniformly distributed, the purpose of reducing differential attack is achieved, and the pixels are subjected to privacy encryption by utilizing a nonlinear equation set image replacement algorithm. The invention further provides a privacy computing system based on the trusted certificate.

Description

Trusted deposit certificate based privacy calculation method and system
Technical Field
The invention relates to the technical field of privacy computation, in particular to a privacy computation method and system based on trusted evidence.
Background
In recent years, with the mature development of computer and network technologies, people's communication modes are changed, and the frequency of information transmission by using networks is higher and higher. Because the network has openness and sharing, hidden danger is caused to the security of multimedia communication, and the multi-purpose face recognition method is used for verification during identity verification, so that how to ensure the privacy of face images in the transmission process becomes a hot topic of current research.
Most of existing face image transmission methods are based on base64 and other simple methods, user privacy data are difficult to guarantee, pixel value distribution of encrypted data obtained by the method is uneven and is easy to be attacked differentially, and a privacy calculation method for face images is provided for solving the problem.
Disclosure of Invention
The invention provides a privacy calculation method based on trusted evidence, which aims to (1) utilize chaotic sequences to carry out pixel confusion on human face image pixels, so that the human face pixel value distribution is uniform, and the aim of reducing differential attack is fulfilled; (2) and performing replacement encryption on the pixels by using a nonlinear equation set image replacement algorithm.
The invention provides a privacy calculation method based on trusted certificate storage, which comprises the following steps:
s1: acquiring a face image, carrying out gray processing on the face image, and carrying out binarization processing on the face gray image to obtain a normalized face image;
s2: generating a chaos sequence, and performing pixel-level chaos processing on the normalized face image by using the chaos sequence to ensure that the distribution of face pixel values is uniformly distributed, thereby achieving the purpose of reducing differential attack;
s3: generating an encryption initial parameter, and encrypting the chaotically processed image by utilizing a nonlinear equation set image replacement algorithm;
s4: when decryption is needed, carrying out decryption processing according to the encrypted initial parameters and carrying out chaotic anti-confusion processing on the decrypted image to obtain an original face image;
determining the chaotic parameters of the chaotic sequence in the step S2 includes:
determining a chaotic sequence parameter updating model:
Figure 740033DEST_PATH_IMAGE001
Figure 368460DEST_PATH_IMAGE002
Figure 607812DEST_PATH_IMAGE003
wherein: the initial value of e is 0 and,
Figure 312594DEST_PATH_IMAGE004
has a value range of
Figure 496450DEST_PATH_IMAGE005
；
Figure 443415DEST_PATH_IMAGE006
Is a chaotic sequence parameter;
updating iteration chaotic sequence parameter updating model
Figure 661907DEST_PATH_IMAGE007
Then, obtain the length of
Figure 537590DEST_PATH_IMAGE007
Chaotic sequence parameter of (1):
Figure 208743DEST_PATH_IMAGE008
wherein:
Figure 319919DEST_PATH_IMAGE007
is the pixel size of the face image.
As a further improvement of the method of the invention:
the step S1 of collecting a face image and performing a graying process on the collected face image includes:
collecting a face image, cutting the collected face image, wherein the size of the cut face image is M multiplied by N pixels, and carrying out gray processing on the collected face image, and the gray processing flow of the face image is as follows:
converting the RGB color pixel value of each pixel point in the image into a gray value to obtain a face gray image, wherein the conversion formula of the RGB color pixel value is as follows:
Figure 656833DEST_PATH_IMAGE009
wherein:
gray (i, j) is the Gray value of the pixel point (i, j), and (i, j) is expressed as the pixel of the ith row and the jth column in the face image;
r (i, j) is the red component value of the pixel (i, j), G (i, j) is the green component of the pixel (i, j), and B (i, j) is the blue component of the pixel (i, j).
In the step S1, the binarization processing is performed on the face grayscale image, and includes:
1) calculating the average gray level mu of the face gray level image:
Figure 952685DEST_PATH_IMAGE010
Figure 861866DEST_PATH_IMAGE011
wherein:
k represents a gray level;
Figure 901366DEST_PATH_IMAGE012
is the probability of the occurrence of a pixel with a gray level k;
Figure 343718DEST_PATH_IMAGE013
the number of pixels with k gray level;
2) taking the gray level m as a segmentation threshold, taking the threshold smaller than the segmentation threshold as a background, and taking the threshold larger than or equal to the segmentation threshold as a foreground, so as to divide the face gray image into the foreground and the background, wherein the background gray value is as follows:
Figure 810471DEST_PATH_IMAGE014
the background number ratio is:
Figure 331583DEST_PATH_IMAGE015
the foreground gray value is:
Figure 659927DEST_PATH_IMAGE016
the foreground number ratio is:
Figure 973097DEST_PATH_IMAGE017
3) calculate the variance of foreground and background:
Figure 597369DEST_PATH_IMAGE018
and modifying the segmentation threshold value m to enable the variance between the foreground and the background to be maximum, wherein the segmentation threshold value at the moment is the optimal segmentation threshold value, carrying out binarization processing on the face gray level image by using the optimal segmentation threshold value, setting the gray level value of the pixel in the face gray level image, which is greater than the optimal segmentation threshold value, to be 1, and setting the gray level value of the pixel, which is smaller than the optimal segmentation threshold value, to be 0, so as to obtain the face image after binarization processing.
In the step S2, the chaos sequence is used to perform pixel-level chaos processing on the face image, including:
the face image pixel-level chaotic processing flow based on the chaotic sequence comprises the following steps:
1) the value of e is set in turn,
Figure 730410DEST_PATH_IMAGE019
m × N is the pixel size of the face image, exchangedE and p lines of the binary face imageeRow pixels, the peThe calculation formula of (2) is as follows:
Figure 596866DEST_PATH_IMAGE020
2) the value of e is set in turn,
Figure 764543DEST_PATH_IMAGE019
m multiplied by N is the pixel size of the face image, and the e column and the q column of the binary face image are exchangedeColumn pixels, said qeThe calculation formula of (2) is as follows:
Figure 556787DEST_PATH_IMAGE021
3) performing pixel diffusion on the binarized face image, wherein Q (i, j) represents pixels of the ith row and the jth column in the binarized face image after line and row exchange, and the pixel diffusion formula is as follows:
Figure 318070DEST_PATH_IMAGE022
Figure 237484DEST_PATH_IMAGE023
wherein:
Figure 744820DEST_PATH_IMAGE024
representing the diffused pixels;
4) scrambling any pixel point Q (i, j) of the image after pixel diffusion, wherein the index value of pixel scrambling is
Figure 724277DEST_PATH_IMAGE025
The pixel coordinates (i ', j') after scrambling are:
Figure 818528DEST_PATH_IMAGE026
Figure 541634DEST_PATH_IMAGE027
5) pixel confusion is carried out on any pixel point after pixel scrambling, wherein a pixel confusion formula is as follows:
Figure 434635DEST_PATH_IMAGE028
wherein:
Figure 319414DEST_PATH_IMAGE025
。
the step S3 of generating the encrypted initial parameters of the image replacement algorithm of the nonlinear equation set includes:
randomly selecting a point of pixel Q from the face image after pixel-level chaos processingiAnd calculating to obtain a pixel QiPixel mean r of adjacent 8 pixels1And a pixel mean r of adjacent 16 pixels2；
Calculating a Hash value of the face image:
Figure 789710DEST_PATH_IMAGE029
Figure 300194DEST_PATH_IMAGE030
wherein:
Figure 31390DEST_PATH_IMAGE031
,key2an encryption key representing a face image;
Figure 103382DEST_PATH_IMAGE032
indicating that only the first 5 bits of Hash value are taken;
Figure 185608DEST_PATH_IMAGE033
the representation converts hexadecimal to decimal;
calculating to obtain a control parameter and an encryption parameter in the encryption initial parameter:
Figure 502713DEST_PATH_IMAGE034
Figure 353994DEST_PATH_IMAGE035
wherein:
Figure 331309DEST_PATH_IMAGE036
representing an encryption parameter;
Figure 776197DEST_PATH_IMAGE037
is an encryption parameter with a value range of [0,0.5 ]]；
Figure 379216DEST_PATH_IMAGE038
Represents a control parameter;
the updating process of setting the encryption parameters is as follows:
Figure 334272DEST_PATH_IMAGE039
Figure 262913DEST_PATH_IMAGE040
wherein:
Figure 539305DEST_PATH_IMAGE041
。
in the step S3, the chaotically processed image is encrypted by using a nonlinear equation set image replacement algorithm, which includes:
performing pixel replacement on any pixel point v (i, j) of the image after the chaos processing:
Figure 211595DEST_PATH_IMAGE042
Figure 746788DEST_PATH_IMAGE043
wherein:
Figure 721698DEST_PATH_IMAGE044
indicating the pixel point at position (i, j) and position
Figure 734653DEST_PATH_IMAGE045
Replacing the position of the pixel point;
repeating the pixel replacement operation until all the pixel points are subjected to position transformation; and obtaining the encrypted face image.
In the step S4, the decrypting process is performed on the encrypted image according to the encrypted initial parameter, and the chaotic anti-aliasing process is performed on the decrypted image, including:
determining the original position of each pixel point according to the encrypted initial parameters, and restoring the image to a face image subjected to pixel-level chaotic processing; and sequentially carrying out inverse operations of pixel confusion, pixel scrambling, pixel diffusion and pixel row-column transformation on the face image subjected to the pixel-level chaos processing to obtain an original face image.
The invention provides a privacy computing system based on trusted evidence, which comprises:
the face image acquisition device is used for acquiring a face image to be encrypted;
the image processor is used for carrying out gray processing on the face image and carrying out binarization processing on the face gray image;
the face image privacy encryption device is used for performing pixel-level chaos processing on the normalized face image by utilizing the chaos sequence and encrypting the chaotically processed image by utilizing a nonlinear equation set image replacement algorithm;
the method for determining the chaotic parameters of the chaotic sequence comprises the following steps:
determining a chaotic sequence parameter updating model:
Figure 695787DEST_PATH_IMAGE001
Figure 110588DEST_PATH_IMAGE002
Figure 364720DEST_PATH_IMAGE003
wherein: the initial value of e is 0 and,
Figure 130551DEST_PATH_IMAGE004
has a value range of
Figure 895376DEST_PATH_IMAGE005
；
Figure 164683DEST_PATH_IMAGE006
Is a chaotic sequence parameter;
updating iteration chaotic sequence parameter updating model
Figure 481395DEST_PATH_IMAGE007
Then, obtain the length of
Figure 721140DEST_PATH_IMAGE007
Chaotic sequence parameter of (1):
Figure 273344DEST_PATH_IMAGE008
wherein:
Figure 413469DEST_PATH_IMAGE007
is the pixel size of the face image.
Compared with the prior art, the invention provides a privacy calculation method based on the trusted certificate, and the technology has the following advantages:
firstly, the scheme provides a face image pixel-level chaos processing scheme, pixel diffusion and pixel confusion processing are carried out on the face image pixels, so that the pixel distribution in the face image is uniformly distributed, and the purpose of reducing differential attack is achieved, the face image pixel-level chaos processing scheme has the following flow, and firstly, a chaos sequence parameter updating model is determined:
Figure 71722DEST_PATH_IMAGE001
Figure 546565DEST_PATH_IMAGE002
Figure 653193DEST_PATH_IMAGE003
wherein: the initial value of e is 0 and,
Figure 38038DEST_PATH_IMAGE046
has a value range of
Figure 555607DEST_PATH_IMAGE047
；
Figure 769944DEST_PATH_IMAGE048
Is a chaotic sequence parameter; updating an iterative chaotic sequence parameter updating model according to the determined chaotic sequence parameter updating model
Figure 398371DEST_PATH_IMAGE049
Then, obtain the length of
Figure 778668DEST_PATH_IMAGE049
Chaotic sequence parameter of (1):
Figure 467138DEST_PATH_IMAGE008
wherein:
Figure 165842DEST_PATH_IMAGE049
is the pixel size of the face image. For the face image to be processed, the e value is set in turn,
Figure 597960DEST_PATH_IMAGE050
，
Figure 957397DEST_PATH_IMAGE049
exchanging e-th line and e-th line of binary face image for pixel size of face image
Figure 567501DEST_PATH_IMAGE051
Row pixels of
Figure 504233DEST_PATH_IMAGE051
The calculation formula of (2) is as follows:
Figure 726661DEST_PATH_IMAGE020
the value of e is set in turn,
Figure 65238DEST_PATH_IMAGE019
，
Figure 642981DEST_PATH_IMAGE052
exchanging e column and e column of binary face image for pixel size of face image
Figure 50698DEST_PATH_IMAGE053
Column pixels of
Figure 90198DEST_PATH_IMAGE053
The calculation formula of (2) is as follows:
Figure 768435DEST_PATH_IMAGE021
performing pixel diffusion on the binarized face image, wherein
Figure 235188DEST_PATH_IMAGE054
Expressing the pixel of the ith row and the jth column in the binarized face image after exchanging rows and columns, wherein the pixel diffusion formula is as follows:
Figure 144849DEST_PATH_IMAGE022
Figure 863407DEST_PATH_IMAGE023
wherein:
Figure 910997DEST_PATH_IMAGE055
representing the diffused pixels; the pixel value of the previous pixel point can be diffused to all the following pixel points through pixel diffusion, so that the pixel value distribution of the human face is uniformly distributed; by diffusing any pixel point of image
Figure 33805DEST_PATH_IMAGE054
Performing scrambling, wherein the index value of pixel scrambling is
Figure 432425DEST_PATH_IMAGE056
Pixel coordinates after scrambling
Figure 797416DEST_PATH_IMAGE057
Comprises the following steps:
Figure 965093DEST_PATH_IMAGE026
Figure 258802DEST_PATH_IMAGE027
pixel aliasing is carried out on any pixel point after pixel scrambling, wherein a pixel aliasing formula is as follows:
Figure 20084DEST_PATH_IMAGE028
wherein:
Figure 673920DEST_PATH_IMAGE056
. The pixels of the face image are partially encrypted by using chaotic-level pixel aliasing and pixel scrambling.
Meanwhile, the scheme provides an image replacement algorithm of a nonlinear equation set, and a pixel Q is randomly selected from a human face image after pixel-level chaos processingiAnd calculating to obtain a pixel QiPixel mean r of adjacent 8 pixels1And a pixel mean r of adjacent 16 pixels2(ii) a Calculating a Hash value of the face image:
Figure 948300DEST_PATH_IMAGE029
Figure 537544DEST_PATH_IMAGE030
wherein:
Figure 628866DEST_PATH_IMAGE058
,
Figure 351971DEST_PATH_IMAGE059
an encryption key representing a face image;
Figure 979392DEST_PATH_IMAGE060
indicating that only the first 5 bits of Hash value are taken;
Figure 129751DEST_PATH_IMAGE061
indicating that hexadecimal is converted to decimal; calculating to obtain a control parameter and an encryption parameter in the encryption initial parameter:
Figure 976878DEST_PATH_IMAGE034
Figure 706936DEST_PATH_IMAGE035
wherein:
Figure 454444DEST_PATH_IMAGE062
representing an encryption parameter;
Figure 775704DEST_PATH_IMAGE063
is an encryption parameter with a value range of
Figure 841617DEST_PATH_IMAGE064
；
Figure 640946DEST_PATH_IMAGE065
Represents a control parameter; the updating process of setting the encryption parameters is as follows:
Figure 367594DEST_PATH_IMAGE039
Figure 610487DEST_PATH_IMAGE040
wherein:
Figure 914430DEST_PATH_IMAGE066
. For any pixel point of chaotically processed image
Figure 769647DEST_PATH_IMAGE067
And (3) carrying out pixel replacement:
Figure 741014DEST_PATH_IMAGE042
Figure 951546DEST_PATH_IMAGE043
wherein:
Figure 257632DEST_PATH_IMAGE044
indicating the pixel point at position (i, j) and position
Figure 398763DEST_PATH_IMAGE045
Replacing positions of the pixel points; repeating the pixel replacement operation until all the pixel points are subjected to position transformation; and obtaining the encrypted face image. Compared with the traditional scheme, the scheme provides the dynamically updated encryption parameters, so that the encryption parameters of each pixel are different, and the difficulty of decryption of the encrypted image is increased.
Drawings
Fig. 1 is a schematic flowchart of a privacy calculation method based on trusted certificate authority according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a trusted certificate authority-based privacy computing system according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
S1: the method comprises the steps of collecting a face image, carrying out gray processing on the face image, and carrying out binarization processing on the face gray image to obtain a normalized face image.
The step S1 of collecting a face image and performing a graying process on the collected face image includes:
collecting a face image, cutting the collected face image, wherein the size of the cut face image is
Figure 100003DEST_PATH_IMAGE049
The pixel is used for carrying out gray processing on the collected face image, and the gray processing flow of the face image is as follows:
converting the RGB color pixel value of each pixel point in the image into a gray value to obtain a face gray image, wherein the conversion formula of the RGB color pixel value is as follows:
Figure 684699DEST_PATH_IMAGE009
wherein:
gray (i, j) is the Gray value of the pixel point (i, j), and (i, j) is expressed as the pixel of the ith row and the jth column in the face image;
r (i, j) is the red component value of the pixel (i, j), G (i, j) is the green component of the pixel (i, j), and B (i, j) is the blue component of the pixel (i, j).
In the step S1, the binarization processing is performed on the face grayscale image, and includes:
1) calculating the average gray level mu of the face gray level image:
Figure 963234DEST_PATH_IMAGE010
Figure 148534DEST_PATH_IMAGE011
wherein:
k represents a gray level;
Figure 828914DEST_PATH_IMAGE012
to a gray level of kThe probability of occurrence of the pixel of (a);
Figure 584512DEST_PATH_IMAGE013
the number of pixels with k gray level;
2) taking the gray level m as a segmentation threshold, taking the threshold smaller than the segmentation threshold as a background, and taking the threshold larger than or equal to the segmentation threshold as a foreground, so as to divide the face gray image into the foreground and the background, wherein the background gray value is as follows:
Figure 350343DEST_PATH_IMAGE014
the background number ratio is:
Figure 82544DEST_PATH_IMAGE015
the foreground gray value is:
Figure 617431DEST_PATH_IMAGE016
the foreground number ratio is:
Figure 543929DEST_PATH_IMAGE017
3) calculate the variance of foreground and background:
Figure 672422DEST_PATH_IMAGE018
modifying the segmentation threshold value m to enable the variance between the foreground and the background to be maximum, wherein the segmentation threshold value at the moment is the optimal segmentation threshold value, performing binarization processing on the face gray level image by using the optimal segmentation threshold value, setting the pixel gray level value which is greater than the optimal segmentation threshold value in the face gray level image as 1, and setting the pixel gray level value which is less than the optimal segmentation threshold value as 0, and obtaining the face image after binarization processing.
S2: and generating a chaotic sequence, and performing pixel-level chaotic processing on the normalized face image by using the chaotic sequence to ensure that the distribution of face pixel values is uniformly distributed, thereby achieving the purpose of reducing differential attacks.
Determining the chaotic parameters of the chaotic sequence in the step S2 includes:
determining a chaotic sequence parameter updating model:
Figure 959047DEST_PATH_IMAGE001
Figure 600638DEST_PATH_IMAGE002
Figure 947305DEST_PATH_IMAGE003
wherein:
the initial value of e is 0 and,
Figure 140258DEST_PATH_IMAGE046
the value range of (1) is (0);
heis a chaotic sequence parameter;
updating the iterative chaotic sequence parameter updating model M multiplied by N times to obtain chaotic sequence parameters with the length of M multiplied by N:
Figure 371519DEST_PATH_IMAGE008
wherein:
m × N is the pixel size of the face image.
In the step S2, the chaos sequence is used to perform pixel-level chaos processing on the face image, including:
the face image pixel-level chaos processing flow based on the chaos sequence comprises the following steps:
1) the value of e is set in turn,
Figure 366151DEST_PATH_IMAGE019
m multiplied by N is the pixel size of the face image, and the e-th line and the p-th line of the binary face image are exchangedeRow pixels, the peThe calculation formula of (2) is as follows:
Figure 883720DEST_PATH_IMAGE020
2) the value of e is set in turn,
Figure 98057DEST_PATH_IMAGE019
m multiplied by N is the pixel size of the face image, and the e column and the q column of the binary face image are exchangedeColumn pixels, the qeThe calculation formula of (2) is as follows:
Figure 992064DEST_PATH_IMAGE021
3) performing pixel diffusion on the binarized face image, wherein Q (i, j) represents pixels of the ith row and the jth column in the binarized face image after line and row exchange, and the pixel diffusion formula is as follows:
Figure 106782DEST_PATH_IMAGE022
Figure 529673DEST_PATH_IMAGE023
wherein:
Figure 493955DEST_PATH_IMAGE024
representing the diffused pixels;
4) scrambling any pixel point Q (i, j) of the image after pixel diffusion, wherein the index value of pixel scrambling is
Figure 67019DEST_PATH_IMAGE025
Scrambled pixel coordinates
Figure 19932DEST_PATH_IMAGE057
Comprises the following steps:
Figure 630036DEST_PATH_IMAGE068
Figure 566768DEST_PATH_IMAGE069
5) pixel aliasing is carried out on any pixel point after pixel scrambling, wherein a pixel aliasing formula is as follows:
Figure 54774DEST_PATH_IMAGE070
wherein:
Figure 393352DEST_PATH_IMAGE025
。
s3: generating an encryption initial parameter, and encrypting the chaotically processed image by using a nonlinear equation set image replacement algorithm.
The step S3 of generating the encrypted initial parameters of the image replacement algorithm of the nonlinear equation set includes:
randomly selecting a point of pixel Q from the face image after pixel-level chaos processingiAnd calculating to obtain a pixel QiPixel mean r of adjacent 8 pixels1And a pixel mean r of adjacent 16 pixels2；
Calculating a Hash value of the face image:
Figure 174357DEST_PATH_IMAGE029
Figure 598385DEST_PATH_IMAGE030
wherein:
Figure 621574DEST_PATH_IMAGE031
,key2an encryption key representing a face image;
Figure 690024DEST_PATH_IMAGE032
indicating that only the first 5 bits of Hash value are taken;
Figure 891198DEST_PATH_IMAGE033
indicating that hexadecimal is converted to decimal;
calculating to obtain a control parameter and an encryption parameter in the encryption initial parameter:
Figure 553255DEST_PATH_IMAGE034
Figure 865287DEST_PATH_IMAGE035
wherein:
Figure 442373DEST_PATH_IMAGE036
representing an encryption parameter;
Figure 283290DEST_PATH_IMAGE037
is an encryption parameter with a value range of [0,0.5 ]]；
Figure 432643DEST_PATH_IMAGE038
Represents a control parameter;
the updating process of setting the encryption parameters is as follows:
Figure 548366DEST_PATH_IMAGE039
Figure 699731DEST_PATH_IMAGE040
wherein:
Figure 508287DEST_PATH_IMAGE041
。
in the step S3, the chaotically processed image is encrypted by using a nonlinear equation set image replacement algorithm, which includes:
performing pixel replacement on any pixel point v (i, j) of the image after the chaos processing:
Figure 613777DEST_PATH_IMAGE042
Figure 798771DEST_PATH_IMAGE043
wherein:
Figure 807572DEST_PATH_IMAGE044
indicating the pixel point at position (i, j) and position
Figure 662395DEST_PATH_IMAGE045
Replacing the position of the pixel point;
repeating the pixel replacement operation until all the pixel points are subjected to position transformation; and obtaining the encrypted face image.
S4: and when decryption is needed, carrying out decryption processing according to the encrypted initial parameters and carrying out chaotic anti-confusion processing on the decrypted image to obtain the original face image.
In the step S4, the decrypting the encrypted image according to the encrypted initial parameter and performing the chaos anti-aliasing process on the decrypted image include:
determining the original position of each pixel point according to the encrypted initial parameters, and restoring the image to a face image subjected to pixel-level chaotic processing; and sequentially carrying out inverse operations of pixel confusion, pixel scrambling, pixel diffusion and pixel row-column transformation on the face image subjected to the pixel-level chaos processing to obtain an original face image.
The invention also provides a privacy computing system based on the trusted deposit certificate. Referring to fig. 2, a schematic diagram of an internal structure of a privacy computing system based on trusted certificate authority according to an embodiment of the present invention is provided.
In the present embodiment, the trusted evidence based privacy computing system 1 at least comprises a face image acquisition device 11, an image processor 12, a face image privacy encryption device 13, a communication bus 14, and a network interface 15.
The face image acquiring apparatus 11 may be a PC (Personal Computer), or a terminal device such as a smart phone, a tablet Computer, or a portable Computer.
Image processor 12 includes at least one type of readable storage medium including flash memory, a hard disk, a multi-media card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. Image processor 12 may, in some embodiments, be an internal storage unit of trusted credit based privacy computing system 1, such as a hard disk of trusted credit based privacy computing system 1. The image processor 12 may also be an external storage device of the trusted memory based privacy computing system 1 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the trusted memory based privacy computing system 1. Further, image processor 12 may also include both internal and external storage units of trusted memory based privacy computing system 1. The image processor 12 can be used not only to store application software installed in the trusted memory based privacy computing system 1 and various kinds of data, but also to temporarily store data that has been output or is to be output.
The face image privacy encryption device 13 may be, in some embodiments, a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor or other data Processing chip including a monitoring Unit for running program codes stored in the image processor 12 or Processing data, such as privacy calculation program instructions 01.
The communication bus 14 is used to enable connection communication between these components.
The network interface 15 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), and is typically used to establish a communication link between the system 1 and other electronic devices.
Optionally, the trusted credit based privacy computing system 1 may further comprise a user interface, which may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface may further comprise a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the trusted credit based privacy computing system 1 and for displaying a visual user interface.
While FIG. 2 shows only the trusted credit based privacy computing system 1 with components 11-15, those skilled in the art will appreciate that the configuration shown in FIG. 2 does not constitute a limitation of the trusted credit based privacy computing system 1, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
In the trusted certificate authority based privacy computing system 1 embodiment shown in fig. 2, image processor 12 has stored therein privacy computing program instructions 01; the steps of the face image privacy encryption device 13 executing the privacy calculation program instructions 01 stored in the image processor 12 are the same as the method for implementing the privacy calculation based on the trusted authority, and are not described here.
Furthermore, an embodiment of the present invention also provides a computer-readable storage medium having stored thereon privacy computing program instructions executable by one or more processors to implement operations comprising:
acquiring a face image, carrying out gray processing on the face image, and carrying out binarization processing on the face gray image to obtain a normalized face image;
generating a chaos sequence, and performing pixel-level chaos processing on the normalized face image by using the chaos sequence to ensure that the distribution of face pixel values is uniformly distributed, thereby achieving the purpose of reducing differential attack;
generating an encryption initial parameter, and encrypting the chaotically processed image by utilizing a nonlinear equation set image replacement algorithm;
and when decryption is needed, carrying out decryption processing according to the encrypted initial parameters and carrying out chaotic anti-confusion processing on the decrypted image to obtain the original face image.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (7)

1. A privacy computing method based on trusted deposit certificate, the method comprising:
s1: acquiring a face image, carrying out gray processing on the face image, and carrying out binarization processing on the face gray image to obtain a normalized face image;
s2: generating a chaos sequence, and performing pixel-level chaos processing on the normalized face image by using the chaos sequence to ensure that the distribution of face pixel values is uniformly distributed, thereby achieving the purpose of reducing differential attack;
s3: generating an encryption initial parameter, and encrypting the chaotically processed image by utilizing a nonlinear equation set image replacement algorithm;
s4: during decryption, carrying out decryption processing according to the encrypted initial parameters and carrying out chaotic anti-confusion processing on the decrypted image to obtain an original face image;
determining the chaotic parameters of the chaotic sequence in the step S2 includes:
determining a chaotic sequence parameter updating model:
Figure 929424DEST_PATH_IMAGE001
Figure 908882DEST_PATH_IMAGE002
Figure 203DEST_PATH_IMAGE003
wherein: the initial value of e is 0 and,
Figure 457730DEST_PATH_IMAGE004
has a value range of
Figure 68839DEST_PATH_IMAGE005
；
Figure 704351DEST_PATH_IMAGE006
Is a chaotic sequence parameter;
updating iteration chaotic sequence parameter updating model
Figure 33701DEST_PATH_IMAGE007
Then, obtain the length of
Figure 278607DEST_PATH_IMAGE007
Chaotic sequence parameter of (1):
Figure 9803DEST_PATH_IMAGE008
wherein:
Figure 550636DEST_PATH_IMAGE007
is the pixel size of the face image;
the face image pixel-level chaos processing flow based on the chaos sequence comprises the following steps:
1) the value of e is set in turn,
Figure 367283DEST_PATH_IMAGE009
，
Figure 433457DEST_PATH_IMAGE007
exchanging e-th line and e-th line of binary face image for pixel size of face image
Figure 753580DEST_PATH_IMAGE010
Row pixels of
Figure 996474DEST_PATH_IMAGE010
The calculation formula of (2) is as follows:
Figure 34837DEST_PATH_IMAGE011
2) the values of e are set in turn, and,
Figure 372277DEST_PATH_IMAGE009
，
Figure 61753DEST_PATH_IMAGE007
exchanging e column and e column of binary face image for pixel size of face image
Figure 459237DEST_PATH_IMAGE012
Column pixels of
Figure 1208DEST_PATH_IMAGE012
The calculation formula of (2) is as follows:
Figure 876760DEST_PATH_IMAGE013
3) performing pixel diffusion on the binarized face image, wherein
Figure 686322DEST_PATH_IMAGE014
Showing the pixel of the ith row and the jth column in the binarized face image after exchanging rows and columns,the pixel diffusion formula is:
Figure 520285DEST_PATH_IMAGE015
Figure 283973DEST_PATH_IMAGE016
wherein:
Figure 228796DEST_PATH_IMAGE017
representing the diffused pixels;
4) any pixel point of image after pixel diffusion
Figure 627285DEST_PATH_IMAGE017
Performing scrambling, wherein the index value of pixel scrambling is
Figure 632150DEST_PATH_IMAGE018
Scrambled pixel coordinates
Figure 866822DEST_PATH_IMAGE019
Comprises the following steps:
Figure 100488DEST_PATH_IMAGE020
Figure 369796DEST_PATH_IMAGE021
5) pixel aliasing is carried out on any pixel point after pixel scrambling, wherein a pixel aliasing formula is as follows:
Figure 800689DEST_PATH_IMAGE022
wherein:
Figure 788237DEST_PATH_IMAGE018
；
the step S3 of generating the encrypted initial parameters of the image replacement algorithm of the nonlinear equation set includes:
randomly selecting a point of pixel from the face image after pixel-level chaos processing
Figure 560015DEST_PATH_IMAGE023
And calculating to obtain pixels
Figure 683829DEST_PATH_IMAGE023
Pixel mean of adjacent 8 pixels
Figure 14185DEST_PATH_IMAGE024
And the mean of the pixels of the adjacent 16 pixels
Figure 489028DEST_PATH_IMAGE025
；
Calculating a Hash value of the face image:
Figure 330076DEST_PATH_IMAGE026
Figure 308397DEST_PATH_IMAGE027
wherein:
Figure 560387DEST_PATH_IMAGE028
,
Figure 771794DEST_PATH_IMAGE029
an encryption key representing a face image;
Figure 134642DEST_PATH_IMAGE030
indicating that only the first 5 bits of Hash value are taken;
Figure 249360DEST_PATH_IMAGE031
indicating that hexadecimal is converted to decimal;
calculating to obtain a control parameter and an encryption parameter in the encryption initial parameter:
Figure 406672DEST_PATH_IMAGE032
Figure 105375DEST_PATH_IMAGE033
wherein:
Figure 537493DEST_PATH_IMAGE034
representing an encryption parameter;
Figure 975559DEST_PATH_IMAGE035
is an encryption parameter with a value range of
Figure 834931DEST_PATH_IMAGE036
；
Figure 240504DEST_PATH_IMAGE037
Represents a control parameter;
the updating process of setting the encryption parameters is as follows:
Figure 719722DEST_PATH_IMAGE038
xi ’ = floor(x n+1×1014) mod M +1
wherein:i=1,2,...,M，
Figure 792720DEST_PATH_IMAGE039
and (3) expressing the mapped chaotic coordinate value, wherein n represents the iteration number, and the maximum value is M.
2. The method according to claim 1, wherein the step S1 of collecting the face image and graying the collected face image includes:
converting the RGB color pixel value of each pixel point in the image into a gray value to obtain a face gray image, wherein the conversion formula of the RGB color pixel value is as follows:
Figure 308146DEST_PATH_IMAGE040
wherein:
Figure 466595DEST_PATH_IMAGE041
is the gray value of the pixel point, and the gray value,
Figure 489784DEST_PATH_IMAGE042
represented as a pixel in the ith row and the jth column in the face image;
Figure 151709DEST_PATH_IMAGE043
is a pixel point
Figure 103616DEST_PATH_IMAGE042
The value of the red color component of (a),
Figure 749361DEST_PATH_IMAGE044
is a pixel point
Figure 45082DEST_PATH_IMAGE042
The green color component of (a) is,
Figure 92672DEST_PATH_IMAGE045
is a pixel point
Figure 199168DEST_PATH_IMAGE046
The blue component of (a).
3. The privacy calculation method based on the trustable certificate as claimed in claim 2, wherein the step of S1 is to perform binarization processing on the face gray-scale image, and includes:
1) calculating average gray of human face gray image
Figure 82942DEST_PATH_IMAGE037
：
Figure 933086DEST_PATH_IMAGE047
Figure 84451DEST_PATH_IMAGE048
Wherein:
k represents a gray level;
Figure 627427DEST_PATH_IMAGE049
is the probability of the occurrence of a pixel with a gray level k;
Figure 467339DEST_PATH_IMAGE050
the number of pixels with k gray level;
2) taking the gray level m as a segmentation threshold, taking the threshold smaller than the segmentation threshold as a background, and taking the threshold larger than or equal to the segmentation threshold as a foreground, so as to divide the face gray image into the foreground and the background, wherein the background gray value is as follows:
Figure 386753DEST_PATH_IMAGE051
the background number ratio is:
Figure 398483DEST_PATH_IMAGE052
the foreground gray value is:
Figure 846782DEST_PATH_IMAGE053
the foreground number ratio is:
Figure 705148DEST_PATH_IMAGE054
3) calculate the variance of foreground and background:
Figure 162674DEST_PATH_IMAGE055
modifying the segmentation threshold value m to enable the variance between the foreground and the background to be maximum, wherein the segmentation threshold value at the moment is the optimal segmentation threshold value, performing binarization processing on the face gray level image by using the optimal segmentation threshold value, setting the pixel gray level value which is greater than the optimal segmentation threshold value in the face gray level image as 1, and setting the pixel gray level value which is less than the optimal segmentation threshold value as 0, and obtaining the face image after binarization processing.
4. The privacy computation method based on the trustable certificate as claimed in claim 1, wherein the step of S3 is to encrypt the chaotically processed image by using a nonlinear equations set image replacement algorithm, and includes:
performing pixel replacement on any pixel point v (i, j) of the image after the chaos processing:
Figure 508205DEST_PATH_IMAGE056
Figure 907831DEST_PATH_IMAGE057
wherein:
Figure 237181DEST_PATH_IMAGE058
indicating the pixel point at position (i, j) and position
Figure 249131DEST_PATH_IMAGE059
The position of the pixel point of (a) is replaced,
Figure 449168DEST_PATH_IMAGE039
expressing the mapped chaotic coordinate value;
repeating the pixel point replacement operation until all the pixel points are subjected to position transformation; and obtaining the encrypted face image.
5. The method according to claim 1, wherein the step S4 of decrypting the encrypted image according to the encrypted initial parameter and performing chaotic anti-aliasing on the decrypted image includes:
determining the original position of each pixel point according to the encrypted initial parameters, and restoring the image to a face image subjected to pixel-level chaotic processing; and sequentially carrying out inverse operations of pixel confusion, pixel scrambling, pixel diffusion and pixel row-column transformation on the face image subjected to the pixel-level chaos processing to obtain an original face image.
6. A trusted deposit certificate based private computing system, the system comprising:
the face image acquisition device is used for acquiring a face image to be encrypted;
the image processor is used for carrying out gray processing on the face image and carrying out binarization processing on the face gray image;
the face image privacy encryption device is used for performing pixel-level chaos processing on the normalized face image by utilizing the chaos sequence and encrypting the chaotically processed image by utilizing a nonlinear equation set image replacement algorithm;
the method for determining the chaotic parameters of the chaotic sequence comprises the following steps:
determining a chaotic sequence parameter updating model:
Figure 19695DEST_PATH_IMAGE001
Figure 836342DEST_PATH_IMAGE002
Figure 120824DEST_PATH_IMAGE060
wherein: the initial value of e is 0 and,
Figure 972105DEST_PATH_IMAGE004
has a value range of
Figure 713534DEST_PATH_IMAGE005
；
Figure 299367DEST_PATH_IMAGE006
Is a chaotic sequence parameter;
updating iteration chaotic sequence parameter updating model
Figure 902387DEST_PATH_IMAGE007
Then, obtain the length of
Figure 875020DEST_PATH_IMAGE007
Chaotic sequence parameters of (1):
Figure 6924DEST_PATH_IMAGE008
wherein:
Figure 548895DEST_PATH_IMAGE007
is the pixel size of the face image.
7. A computer-readable storage medium having stored thereon privacy computing program instructions executable by one or more processors to perform the steps of the trusted deposit based privacy computing method of any one of claims 1-5.
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