CN111241561B - User certifiable outsourcing image denoising method based on privacy protection - Google Patents
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Abstract
The invention relates to a user certifiable outsourcing image denoising method based on privacy protection, which comprises the following steps: a trusted third party TTP to a content owner CO, an authorized user AU and two edge compute servers ES1、ES2Distributing the related keys; the CO first encrypts the noisy image using the key assigned by the TTP and sends the encrypted image to the first edge computing server ES1(ii) a Second edge compute server ES2Assisting a first edge computing server ES1The ciphertext image is denoised, and the result obtained by calculation is sent to a first edge calculation server ES1(ii) a AU makes image use request to corresponding CO, and calculates server ES from first edge1And acquiring a corresponding de-noised ciphertext image, and authorizing the AU to decrypt and recover the required plaintext de-noised image under the help of a private key of the AU. The invention ensures the safety of the user privacy data and provides the image denoising service; and the local calculation and communication overhead of the user is reduced, and the ciphertext denoising effect is almost equal to the performance of a plaintext domain.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a user authenticable outsourcing image denoising method based on privacy protection.
Background
At present, computer vision technology has been widely applied in the fields of image classification, object detection, semantic segmentation, and the like. In these fields, there is a certain requirement on image quality, but in reality, digital images obtained by interference of the imaging device itself or many external environments are often noisy, and if the noisy images are directly used as samples for research, the research results may be negatively affected. Therefore, before image processing, a denoising preprocessing operation is often required to be performed on the sample image to obtain sample data with higher quality.
In recent years, with the popularization of mobile imaging devices such as smart phones and digital cameras and the rapid development of multimedia technologies, the number of images has increased explosively, which brings great challenges to image storage and processing for users with limited resources. In recent years, cloud computing and services thereof are widely applied to many fields, are generally accepted and used by people, and also bring great commercial value to cloud computing service providers. The global public cloud services market promulgated in Gartner is predicted to grow by 17% by 2020, with a total of 2664 billion dollars, higher than 2227 billion dollars in 2019. The cloud computing technology solves the problem of resource limitation of users, and the cost of large-scale data storage and processing is greatly reduced by means of self mass storage and strong computing capacity. In this case, users tend to send their multimedia data to cloud computing for storage and processing. But the problem is that the storage position of the external packet data and whether the data is lost or even tampered are unknown to the user, which is easy to cause the user to worry about the data privacy security. Encrypting data directly with a conventional encryption algorithm prior to outsourcing is a common method of ensuring confidentiality of data. However, it may prevent further processing of the encrypted data, such as image denoising. A simple scheme for denoising secure images is: downloading the encrypted noisy image, locally decrypting the encrypted noisy image into a plaintext noisy image, and then directly performing denoising operation by using a denoising method. However, such operations may increase the computational cost and communication overhead for the user. Therefore, it is very important to directly denoise the ciphertext image without revealing the privacy of the user.
Recently, signal processing on data of encrypted text and images has become a popular research field, and various research branches such as ciphertext image sharing, text searchable encryption, ciphertext domain image retrieval, ciphertext domain invertible information hiding, and the like have been successively appeared. Relatively, there is relatively little research on secure image denoising on ciphertext images. The existing ciphertext domain image denoising methods mainly comprise three types, the first type is based on a secret sharing technology, the representative method is a safe wavelet denoising method based on secret sharing provided in Saghaian, but threshold normalization after each multiplication operation can be completed only by mutual communication of related parties, and the calculation and communication cost of a user can be increased; the second method is based on a secure multi-party computing technology, and the representative is that Pedrouzo-Ulloa et al provides a wavelet denoising scheme based on lattice codes, which solves the homomorphic computing problem of single-round filtering and threshold operation, but has low efficiency in the non-local mean image denoising algorithm. Another work is Zheng et al [4] proposed to utilize an external cloud database to assist image security denoising, the scheme is based on two cloud servers, one for storing an encrypted image block database, and the other for generating a garbled circuit and sending it to the image cloud database to perform comparison operations. For a large image database, the communication load between the servers is quite huge. The third kind is based on homomorphic encryption technology, and its representative work is Hu et al propose a dual-coding scheme based on Paillier additive homomorphism and JL distance preserving transformation to perform non-local image denoising. However, the introduction of the Paillier public key encryption system easily causes the expansion of ciphertext data and the increase of computation complexity. In addition, in the scheme, the pixel weight requires that the user side performs the JL transformation operation in advance, which is easy to affect the denoising precision and the user experience. Based on the scheme, Hu et al further provides a random non-local mean denoising algorithm based on double servers. Compared with the first scheme, the scheme reduces ciphertext expansion and reduces communication overhead of the user and the cloud server. However, in the new scheme, the JL transformation still needs to be performed by the user side, and the user needs to interact with two cloud servers in the denoising process.
The above three types of ciphertext denoising schemes are all based on a cloud server (see fig. 1), which easily causes processing time delay and high network bandwidth requirements, and affects user experience. In addition, the existing scheme does not consider the problem of outsourced certification for the user to perform authorization and revocation operations from the viewpoint of the practicability of the system. Currently, a feasible method is still lacked in the field, and a low-delay user-certifiable outsourced image denoising scheme for privacy protection can be supported.
Disclosure of Invention
In view of the above, the present invention provides a user certifiable outsourced image denoising method based on privacy protection, which ensures the security of user privacy data and provides an image denoising service; and the local calculation and communication overhead of the user is reduced, and the ciphertext denoising effect is almost equal to the performance of a plaintext domain.
The invention is realized by adopting the following scheme: a user authenticable outsourcing image denoising method based on privacy protection comprises a trusted third party TTP, a content owner CO, an authorized user AU and a first edge computing server ES1A second edge computing server ES2The method comprises the following steps:
step S1: the trusted third party TTP gives the content owner CO, the authorized user AU and the two edge computing servers ES1、ES2Distributing the related keys;
step S2: the content owner CO first encrypts the noisy image using a key assigned by the trusted third party TTP and sends the encrypted image to the first edge computing server ES1(ii) a Second edge compute server ES2Assisting a first edge computing server ES1The ciphertext image is denoised, and the result obtained by calculation is sent to a first edge calculation server ES1；
Step S3: the authorized user AU makes an image use request to the corresponding content owner CO, andcomputing a server ES from a first edge1And acquiring a corresponding de-noised ciphertext image, and authorizing the AU to decrypt and recover the required plaintext de-noised image under the help of a private key of the AU.
Further, step S1 is specifically: the trusted third party TTP generates a master key k and assigns its private key k to the data owner COCOAssigning its private key k to an authorized user AUAUTo the first edge computing server ES1Assign its private key k'CO、
k′AUTo the second edge compute server ES2Distributing its private key
Wherein the private key satisfies: k is a radical ofCO·k′CO＝k、
k′AU·kAUK and
further, the step S2 specifically includes the following steps:
step S21: the content owner CO assigns to it a key k according to the TTPCOFor own image { I1,I2,...,IMEncrypting one by one, wherein the size of the image is mxn; let a given picture be It(t ∈ { 1.,. M }), whose pixel is denoted vt(i, j), wherein i ∈ {1, 2.., m }, j ∈ {1, 2.., n }, and (i, j) represents a position of a pixel in a corresponding image; v. oft(i, j) the encrypted data is marked as ct(i, j), the encryption process is shown as formula (1):
in the formula, x1,...,xgThe value of (d) is obtained by the Chinese remainder theorem, diag (v)t(i,j),x1,...,xg) Is formed by g +1 variables vt(i,j),x1,...,xgThe formed diagonal matrix;
step S22: after the data owner CO has encrypted all the images, the encrypted noisy images are uploaded to a first edge computing server ES1Before this step is completed, the data owner CO calculates the server ES for the first edge1The service request sent out has two forms, one is a request for only storing and processing images which are unwilling to share, the other is a request for carrying out ciphertext denoising processing and user authorization verification on images which are willing to share, the former case does not carry out any processing, and for the latter case, when the authorization verification of an authorized user passes, the first edge computing server ES1Key k 'distributed by TTP side'COThe encrypted uploaded image is further processed, i.e. re-encrypted into another ciphertext form Ct(i, j), namely:
at this time Ct(i, j) is vt(i, j) the data encrypted by the key k has a corresponding key transformation mechanism as shown in equation (3):
the square of the euclidean distance in the image ciphertext state is further calculated by equation (3): for ciphertext image ItPixel v oft(i, j) -centric search window Nt(i, j), and by pixel vt(a, b) centered neighborhood window Nt(a, b) where the window size d × d, the ciphertext E (Dist (N) of the square of the Euclidean distance between them is calculatedt(i,j),Nt(a, b)), k) is as shown in equation (4):
again, by the cryptographic algorithm used in the system having additive and multiplicative homomorphism, i.e. satisfying equations (5) and (6), equation (4) is further reduced to equation (7):
E(m1,k)+E(m2,k)＝E(m1+m2,k)； (5)
E(m1,k)·E(m2,k)＝E(m1·m2,k)； (6)
step S23: first edge calculation Server ES in equation (7)1The Euclidean distance square of the similar block pixel under the encryption of the main key k can be obtained, and at the moment, the first edge computing server ES1By another private key of its own
The ciphertext data is subjected to similarity transformation into the calculation result of equation (8), and the second edge calculation server ES2Has a key of ES1
And then the square value of the plain text Euclidean distance is obtained, and the steps are as the following formula (9):
in the formula (I), the compound is shown in the specification,
is a similarity transformation function that satisfies the relationship: if there is any ciphertext as C ═ E (x, k), then
(·)00Taking a first element of the matrix;
step S24: the second edge computing server ES2 obtains the similar weight value w according to the plain text Euclidean distance of the similar pixel blockst((i, j), (a, b)), where wt((i, j), (a, b)) represents the weights of two similar pixels of the pixel in the ith row and the jth column and the pixel in the a row and the b column, and the weight value w in the ciphertext state is obtained according to the formulas (10) and (11)t((i,j),(a,b))：
In the formula, | · the luminance | |2Is the euclidean distance, h is the coefficient used to control the attenuation of the weights, Ω is the similar pixel search window; second edge compute server ES2After obtaining the weight values of the similar pixel blocks, the server ES is calculated for preventing the first edge1The weight value in the plaintext can be obtained, and the encryption processing is carried out:
wherein A is a scaling factor;
step S25: second edge compute server ES2Sending the weighted value in the ciphertext state to the first edge computing server ES1(ii) a Then ES1Performs re-encryption, i.e. calculates equation (13), second edge calculation server ES2After key conversion, a weighted value ciphertext encrypted by the main key is obtainedFormula (14):
E(A·wt((i,j),(a,b)),k)＝k-1·diag(A·wt((i,j),(a,b)),x1,...,xg)·k； (14)
step S26: when obtaining the weight value of the encrypted main key k, the first edge computing server ES1Can encrypt the image ItPerforming non-local mean filtering processing, so as to complete the denoising operation, as shown in equation (15):
further, in step S23, the second edge calculation server ES2After obtaining the distance in the ciphertext state of equation (8), performing key transformation to obtain the squared Euclidean distance in the plaintext state, as equation (9), which is the pair of ES2In other words, the approximate outline of the image can be derived by a method in which the first edge computing server ES takes into account such unsafe factors1Randomly selecting cipher text pixels to form a plurality of blocks, calculating the square of the Euclidean distance of cipher texts between the blocks by using a formula (7), using the square as noise to cover the real cipher text distance between the blocks, wherein the additional Euclidean distance only has a first edge to calculate the server ES1It is known that even ES2The distance in the plaintext can be obtained, and the true statistical distribution of the original Euclidean distance square value can not be estimated.
Further, step S3 is specifically: first edge compute server ES1And (3) carrying out data similarity transformation on the denoised ciphertext image, as shown in a formula (16), and then returning to a corresponding authorized user AU:
AU of authorized user using its correspondenceKey k ofAUAnd finally decrypting to recover the required plaintext de-noised image, namely:
in the formula (I), the compound is shown in the specification,
representing an image ItThe plaintext pixel values at location (i, j) after denoising.
Further, when the authorized user AU is a plurality of users with different levels, each user is assigned an id number, and q levels are set, that is, the vip1,vip2,...,vipqWhen an authorized user AU submits a de-noising service request, the first edge calculation server ES1Firstly, verifying the access authority of a user, and if the user passes the verification, executing denoising service of a corresponding level; if not, ES1The service will be denied.
Further, the authorization verification specifically includes:
step SA: assignment of kappa to content owner CO by trusted third party TTPCOAssigning kappa to authorized user AUAU、κ′AUTo the first edge computing server ES1Dispensing
And meets the requirements that: kappaCO·κAU＝κ、
Step SB: AU submission (id) of authorized userAU,vipAU) Requesting the corresponding content owner for the right to use the image, if the data owner CO agrees with the user, a plaintext certificate T ═ CO, id, will be generated for the authorized user AUAU,vipAU) And use the private informationKey kappaCOThe certificate is encrypted, as in formula (18), d is recorded
Finally, the data owner CO respectively sends the plaintext version and the ciphertext version of the certificate to the first edge computing server ES1And an authorized user AU:
step SC: when an AU is authorized to obtain credentials
Then, use the private key κAUIt is encrypted again, i.e.:
at this time, the authorized user AU needs to perform similarity transformation once, and the specific process is as shown in equation (20):
finally, the authorized user AU will send to the first edge computing server ES1And a denoising service image use application is provided for the application;
step SD: when the first edge calculates the server ES1Using the key upon receiving a user request
To pair
Performing decryption operation, specifically as formula (21):
if the result of the calculation of the formula (21) is completed and the certificate
If the AU is equal to the AU, the AU is authorized to pass the user authority verification and enjoy the denoising service right; if the results are not equal, the first edge computing server ES1The current user cannot be authorized, i.e. the user cannot enjoy the denoising service, and the verification process is ended.
Further, if the data owner CO wants to perform outsource revocation certification, the method includes the following steps:
step SA: trusted third party TTP will upsilonCOIs distributed to the data owner CO, will
ES assigned to first edge computing server1Will be
To the second edge compute server ES 2; and meets the requirements of:
step SB: the data owner CO collects information of users who are not using the resource illegally and generates a corresponding certificate R ═ CO, rev, idAU,vipAU) Where rev denotes a revocation operation, the data owner CO then uses its own key vCOThe encryption certificate R is represented by formula (22):
the data owner CO converts the plaintext version R and the ciphertext version of the certificate
Respectively sent to a first edge computing server ES1And a second edge computing server ES2；
Step SC: when receiving the withdrawal request, the second edge computing server ES2To pair
And carrying out encryption again, namely:
at this time, the second edge computing server ES2A similarity transformation is required, as shown in equation (24):
second edge compute server ES2Will be provided with
Send to the first edge computing server ES1In the private key
Assisted first edge compute server ES1And executing decryption operation, specifically as (25):
by comparison
And R, a first edge computing server ES1The revocation validity can be judged, and if the revocation validity is equal, the first edge computing server ES1Performing a revocation operation on the corresponding user; if the results are not equal, the first edge computing server ES1Continue to respond to the de-noising service enjoyed on the user's certificate content.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention requires no interaction between the server and the user. In the scheme of the invention, the image content owner only needs to complete the encryption operation of the image locally, and the denoising work is completed by the edge computing server with more outstanding performance advantages, thereby being convenient for the image content owner.
2. The invention has low noise removing precision loss. In the scheme of the invention, JL transformation operation is not needed, the denoising precision loss is reduced, and the denoising effect is basically the same as that of a plaintext domain.
3. The invention supports multi-user multi-key management, and designs a novel key conversion protocol. It allows different users to have their own keys, greatly enhancing system security.
4. Low computation delay and low broadband cost. The invention introduces the edge calculation technology, reduces the intermediate transmission process and improves the overall processing efficiency of the system; in the scheme, the edge calculation nearby processing does not need to exchange data with a cloud server, and the requirement of a user network broadband is reduced.
5. The invention also includes a user authorization and decommissioning mechanism. Based on a key conversion mechanism, the invention designs a safe user authorization and revocation outsourceable mechanism, which is suitable for multi-level multi-user scenes. It allows the server to authenticate correctly users performing different levels of authorization without knowledge of the plaintext content; and simultaneously, the server is allowed to execute denoising service revocation operation on the user specified by the image content owner.
Drawings
FIG. 1 is a prior art ciphertext domain denoising framework.
Fig. 2 is a basic structure of ciphertext domain denoising in the embodiment of the present invention.
FIG. 3 is a block diagram of the overall framework for ciphertext domain denoising according to the embodiment of the present invention.
Fig. 4 illustrates a mechanism for outsourced authentication for authorization and revocation of multi-level and multi-user in an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure herein. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 2 and fig. 3, the present embodiment provides a user authenticatable outsourcing image denoising method based on privacy protection, including a trusted third party TTP, a content owner CO, an authorized user AU, and a first edge computing server ES1A second edge computing server ES2The method comprises the following steps:
step S1: the trusted third party TTP gives the content owner CO, the authorized user AU and the two edge computing servers ES1、ES2Distributing the related keys;
step S2: the content owner CO first encrypts the noisy image using a key assigned by the trusted third party TTP and sends the encrypted image to the first edge computing server ES1(ii) a Second edge compute server ES2Assisting a first edge computing server ES1The ciphertext image is denoised, and the result obtained by calculation is sent to a first edge calculation server ES1(ii) a While the process is generated, all the user authorization certificates and corresponding ciphertext data which are allowed to be accessed are uploaded to the first edge computing server ES1。
Step S3: the authorized user is equivalent toThe consumer of the system, who needs a sufficiently clear set of images for experimental or commercial use. The authorized user AU will therefore make an image use request to the corresponding content owner CO and calculate the server ES from the first edge1And acquiring a corresponding de-noised ciphertext image, and authorizing the AU to decrypt and recover the required plaintext de-noised image under the help of a private key of the AU.
In the present embodiment, the first edge computing server ES1The method mainly responds to a storage or denoising processing request of a content owner, and provides a storage process, denoising calculation and a denoising result for the content owner. Furthermore, the first edge computing server ES1And the system is responsible for responding to the validity verification of the access right of the user. Second edge compute server ES2Is responsible for assisting the edge computing server 1 to carry out ciphertext image denoising, and the result obtained by computing is sent to the first edge computing server ES1。
In this embodiment, the TTP calculates and distributes keys belonging to its own according to the number of authorized users and edge computing servers, so that the use of independent keys greatly improves the security of the system. Specifically, at the beginning of the system, the TTP allocates corresponding keys to the content owner, the authorized user, and the edge computing server, as shown in (r) of fig. 3, step S1 specifically includes: the trusted third party TTP generates a master key k and assigns its private key k to the data owner COCOAssigning its private key k to an authorized user AUAUThe first edge compute server ES1 is assigned its private key k'CO、
To the second edge compute server ES2Distributing its private key
Wherein the private key satisfies: k is a radical ofCO·k′CO＝k、
And
k is the private key of TTP, CO, ES1、ES2AU cannot acquire the key k. The private keys are TTP distributed to CO and ES through a secure channel1、ES2、AU。
In this embodiment, the step S2 specifically includes the following steps:
step S21: like that in fig. 3, the content owner CO assigns to it a key k according to the TTPCOFor own image { I1,I2,...,IMEncrypting one by one, wherein the size of the image is mxn; let a given picture be It(t ∈ { 1.,. M }), whose pixel is denoted vt(i, j), wherein i ∈ {1, 2.., m }, j ∈ {1, 2.., n }, and (i, j) represents a position of a pixel in a corresponding image; v. oft(i, j) the encrypted data is marked as ct(i, j), the encryption process is shown as formula (1):
in the formula, x1,...,xgThe value of (d) is obtained by the Chinese remainder theorem, diag (v)t(i,j),x1,...,xg) Is formed by g +1 variables vt(i,j),x1,...,xgThe formed diagonal matrix;
step S22: as shown in fig. 3, after the data owner CO encrypts all the images, the encrypted noisy images are uploaded to the first edge computing server ES1Before this step is completed, the data owner CO calculates the server ES for the first edge1The service request sent out has two forms, one is a request for only storing and processing images which are unwilling to share, the other is a request for carrying out ciphertext denoising processing and user authorization verification on images which are willing to share, the former condition is not processed, and for the latter condition, after the authorization verification of an authorized user is passed, the second condition isAn edge computing server ES1Key k 'distributed by TTP side'COThe encrypted uploaded image is further processed, i.e. re-encrypted into another ciphertext form Ct(i, j), namely:
at this time Ct(i, j) is vt(i, j) the data encrypted by the key k has a corresponding key transformation mechanism as shown in equation (3):
the square of the euclidean distance in the image ciphertext state is further calculated by equation (3): for ciphertext image ItC is the pixel vt(i, j) -centric search window Nt(i, j), and by pixel vt(a, b) centered neighborhood window Nt(a, b) where the window size d × d, the ciphertext E (Dist (N) of the square of the Euclidean distance between them is calculatedt(i,j),Nt(a, b)), k) is as shown in equation (4):
again, by the cryptographic algorithm used in the system having additive and multiplicative homomorphism, i.e. satisfying equations (5) and (6), equation (4) is further reduced to equation (7):
E(m1,k)+E(m2,k)＝E(m1+m2,k)； (5)
E(m1,k)·E(m2,k)＝E(m1·m2,k)； (6)
here, the calculation of the squared euclidean distance can be outsourced to any party who does not know the secret key because all steps are performed in the ciphertext state and the calculating party cannot obtain any plaintext information without the secret key.
Step S23: as of (r) in FIG. 3, the first edge calculation Server ES in equation (7)1The Euclidean distance square of the similar block pixel under the encryption of the main key k can be obtained, and at the moment, the first edge computing server ES1By another private key of its own
The ciphertext data is subjected to similarity transformation into the calculation result of equation (8), and the second edge calculation server ES2Has a first electrode and an ES1The corresponding key is
And then the square value of the plain text Euclidean distance is obtained, and the steps are as the following formula (9):
in the formula (I), the compound is shown in the specification,
is a similarity transformation function that satisfies the relationship: if there is any ciphertext as C ═ E (x, k), then
(·)00Taking a first element of the matrix;
step S24: second edge compute server ES2Obtaining similar weight value w according to plaintext Euclidean distance of similar pixel blockst((i, j), (a, b)), where wt((i, j), (a, b)) indicates the weights of two similar pixels of the pixel in the ith row and j column and the pixel in the a row and b column,obtaining the weighted value w in the ciphertext state according to the formulas (10) and (11)t((i,j),(a,b))：
The denoising algorithm adopted by the embodiment is a non-local mean denoising algorithm in a plain text domain. In the formula, | · the luminance | |2Is the euclidean distance, h is the coefficient used to control the attenuation of the weights, Ω is the similar pixel search window; second edge compute server ES2After obtaining the weight values of the similar pixel blocks, the server ES is calculated for preventing the first edge1The weight value in the plaintext can be obtained, and the encryption processing is carried out:
wherein A is a scaling factor; the encryption algorithm employed here is based on large integers, so the ES2Before encryption, integer preprocessing needs to be performed on the weight value, namely multiplying by a scaling factor A. Obviously, a certain error is introduced in the process, and certainly, selecting a sufficiently large a is a simple and effective solution, but in order to enable the experiment to decrypt a correct denoising value, a needs to satisfy a < 2N/28Where N is the number of bits of the encryption key. Generally, the value of a cannot be too large or too small, and a relatively suitable value needs to be selected according to actual conditions
Step S25: e.g., # in FIG. 3, the second edge compute server ES2Sending the weighted value in the ciphertext state to the first edge computing server ES1(ii) a Then ES1Performs re-encryption, i.e. calculates equation (13), second edge calculation server ES2After key conversion, a weighted value ciphertext encrypted by the master key is obtainedAs shown in formula (14):
E(A·wt((i,j),(a,b)),k)＝k-1·diag(A·wt((i,j),(a,b)),x1,...,xg)·k； (14)
step S26: when the weight value of the encrypted master key k is obtained, the first edge computing server ES1Can encrypt the image ItPerforming non-local mean filtering processing, so as to complete the denoising operation, as shown in equation (15):
in the present embodiment, in step S23, the second edge calculation server ES2After obtaining the distance in the ciphertext state of equation (8), performing key transformation to obtain the squared Euclidean distance in the plaintext state, as equation (9), which is the pair of ES2In other words, the approximate outline of the image can be derived by a method in which the first edge computing server ES takes into account such unsafe factors1Randomly selecting cipher text pixels to form a plurality of blocks, calculating the square of the Euclidean distance of cipher texts between the blocks by using a formula (7), using the square as noise to cover the real cipher text distance between the blocks, wherein the additional Euclidean distance only has a first edge to calculate the server ES1It is known that even ES2The distance in the plaintext can be obtained, and the true statistical distribution of the original Euclidean distance square value can not be estimated.
In this embodiment, as shown in fig. 3 c and b, step S3 specifically includes: first edge compute server ES1And (3) carrying out data similarity transformation on the denoised ciphertext image, as shown in a formula (16), and then returning to a corresponding authorized user AU:
authorized user AU uses its corresponding key kAUAnd finally decrypting to recover the required plaintext de-noised image, namely:
in the formula (I), the compound is shown in the specification,
representing an image ItThe plaintext pixel values at location (i, j) after denoising. In the process of equation (12), the quantization process is introduced in this embodiment, so that when the authorized user decrypts, we need to perform inverse quantization operation, and the steps and quantization process are inverse.
In this embodiment, in a real multi-level and multi-user system, when the authorized user AU is a plurality of users with different levels, each user is assigned an id number, and q levels, that is, vip, exist1,vip2,...,vipqThe service enjoyed by each different level of users is defined to be different, and is mainly embodied in the number of the denoised images which can be owned at one time and the time period which can be owned with the denoised service, such as: vip1The user can be allowed to have the number of the denoised images within 2000 at a time, and the time period for having the denoising service can be allowed to: 20191201- - -20200110.
As shown in the upper part of FIG. 4, assuming that the CO has many images available for experiment, he would like to earn profit in the form of sharing images to users of different levels, but since these digital images are noisy due to imaging equipment or external environmental interference and his own computing resources are limited, he would choose to outsource denoising operations and authorize certifications to the ES1. When an authorized user AU submits a de-noising service request, a first edge calculation server ES1Firstly, verifying the access authority of a user, and if the user passes the verification, executing denoising service of a corresponding level; if not, ES1The service will be denied.
In this embodiment, the authorization verification specifically includes:
step SA: trusted third party TTP assigns kappa to content owner COCOAssigning kappa to authorized user AUAU、κ′AUTo the first edge computing server ES1Dispensing
And meets the requirements that: kappaCO·κAU＝κ、
Step SB: AU submission (id) of authorized userAU,vipAU) Requesting the corresponding content owner for the right to use the image, if the data owner CO agrees with the user, a plaintext certificate T ═ CO, id, will be generated for the authorized user AUAU,vipAU) And using the private key kCOThe certificate is encrypted, as in formula (18), d is recorded
Finally, the data owner CO respectively sends the plaintext version and the ciphertext version of the certificate to the first edge computing server ES1And an authorized user AU:
step SC: when an AU is authorized to obtain credentials
Then, use the private key κAUIt is encrypted again, i.e.:
at this time, the authorized user AU needs to perform similarity transformation once, and the specific process is as shown in equation (20):
finally, the authorized user AU will send to the first edge computing server ES1And a denoising service image use application is provided for the application;
step SD: when the first edge calculates the server ES1Using the key upon receiving a user request
To pair
Performing a decryption operation, specifically as in formula (21):
if the result of the calculation of the formula (21) is completed and the certificate
If the AU is equal to the AU, the AU is authorized to pass the user authority verification and enjoy the denoising service right; if the results are not equal, the first edge computing server ES1The current user cannot be authorized, i.e. the user cannot enjoy the denoising service, and the verification process is ended.
As shown in the lower half of fig. 4, the present embodiment is divided into two cases regarding the revocation case. (I) In order to save the cost, part of users may temporarily decide to cancel the denoising service. In this case, the scheme of this embodiment allows the user to propose a service revocation application to the CO, and the specific implementation of the subsequent revocation process can refer to the outsourcing authorization authentication process; (II), there are actually some users who, in order to earn their own interest, may not agree by CO to be from ES1The acquired denoised image is illegally utilized and widely spread. Thus, the present embodiment allows the CO to have the right to force termination of cooperation with a malicious user,meanwhile, in consideration of the convenience of the CO, an outsource revocation certification mechanism is designed to guarantee the rights and interests of the CO, and in this embodiment, if the data owner CO needs to perform outsource revocation certification, the method includes the following steps:
step SA: trusted third party TTP will upsilonCOIs distributed to the data owner CO, will
ES assigned to first edge computing server1Will be
ES assigned to the second edge compute server2(ii) a And meets the requirements that:
step SB: the data owner CO collects information of users who are not using the resource illegally and generates a corresponding certificate R ═ CO, rev, idAU,vipAU) Where rev denotes a revocation operation, the data owner CO then uses its own key vCOThe encryption certificate R is represented by formula (22):
the data owner CO sends the plaintext version R and the ciphertext version of the certificate
Respectively sent to a first edge computing server ES1And a second edge computing server ES2；
Step SC: when receiving the withdrawal request, the second edge computing server ES2For is to
Is encrypted again to obtain：
At this time, the second edge computing server ES2A similarity transformation is required, as shown in equation (24):
second edge compute server ES2Will be provided with
Send to the first edge computing server ES1In the private key
Assisted first edge compute server ES1And executing decryption operation, specifically as (25):
by comparison
And R, a first edge computing server ES1The revocation validity can be judged, and if the revocation validity is equal, the first edge computing server ES1Performing a revocation operation on the corresponding user; if the results are not equal, the first edge computing server ES1Continue to respond to the de-noising service enjoyed on the user's certificate content.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention will still fall within the protection scope of the technical solution of the present invention.
Claims (7)
1. A user authenticable outsourcing image denoising method based on privacy protection is characterized by comprising a trusted third party TTP, a content owner CO, an authorized user AU and a first edge computing server ES1A second edge computing server ES2The method comprises the following steps:
step S1: the trusted third party TTP gives the content owner CO, the authorized user AU and the two edge computing servers ES1、ES2Distributing the related keys;
step S2: the content owner CO first encrypts the noisy image using a key assigned by the trusted third party TTP and sends the encrypted image to the first edge computing server ES1(ii) a Second edge compute server ES2Assisting a first edge computing server ES1The ciphertext image is denoised, and the result obtained by calculation is sent to a first edge calculation server ES1；
Step S3: the authorized user AU makes an image use request to the corresponding content owner CO and calculates a server ES from a first edge1Acquiring a corresponding de-noised ciphertext image, and authorizing a user AU to decrypt and recover the required plaintext de-noised image under the help of a private key of the user AU;
the step S2 specifically includes the following steps:
step S21: the content owner CO assigns to it a key k according to the TTPCOFor own image { I1,I2,...,IMEncrypting one by one, wherein the size of the image is mxn; let a given picture be It(t ∈ { 1.,. M }), whose pixel is denoted vt(i, j), wherein i ∈ {1, 2.., m }, j ∈ {1, 2.., n }, and (i, j) represents a position of a pixel in a corresponding image; v. oft(i, j) the encrypted data is recorded as ct(i, j), the encryption process is shown as formula (1):
in the formula, x1,...,xgThe value of (d) is obtained by the Chinese remainder theorem, diag (v)t(i,j),x1,...,xg) Is formed by g +1 variables vt(i,j),x1,...,xgThe formed diagonal matrix;
step S22: after the data owner CO has encrypted all the images, the encrypted noisy images are uploaded to a first edge computing server ES1Before this step is completed, the data owner CO calculates the server ES for the first edge1The service request sent out has two forms, one is a request for only storing and processing images which are unwilling to share, the other is a request for carrying out ciphertext denoising processing and user authorization verification on images which are willing to share, the former case does not carry out any processing, and for the latter case, when the authorization verification of an authorized user passes, the first edge computing server ES1Key k 'distributed by TTP side'COThe encrypted uploaded image is further processed, i.e. re-encrypted into another ciphertext form Ct(i, j), namely:
at this time Ct(i, j) is vt(i, j) the data encrypted by the key k has a corresponding key transformation mechanism as shown in equation (3):
the square of the euclidean distance in the image ciphertext state is further calculated by equation (3): for ciphertext image ItPixel v oft(i, j) -centric search window Nt(i, j), and by pixel vt(a, b) centered neighborhood window Nt(a, b) wherein the window size d x d, calculated between themCipher text E (Dist (N) of Euclidean distance squaret(i,j),Nt(a, b)), k) is as shown in equation (4):
again, by the cryptographic algorithm used in the system having additive and multiplicative homomorphism, i.e. satisfying equations (5) and (6), equation (4) is further reduced to equation (7):
E(m1,k)+E(m2,k)＝E(m1+m2,k)； (5)
E(m1,k)·E(m2,k)＝E(m1·m2,k)； (6)
step S23: first edge calculation Server ES in equation (7)1The Euclidean distance square of the similar block pixel under the encryption of the main key k can be obtained, and at the moment, the first edge computing server ES1By another private key of its own
The ciphertext data is subjected to similarity transformation into the calculation result of equation (8), and the second edge calculation server ES2Has a first electrode and an second electrode1The corresponding key is
And then the square value of the plain text Euclidean distance is obtained, and the steps are as the following formula (9):
in the formula (I), the compound is shown in the specification,
is a similarity transformation function that satisfies the relationship: if there is any ciphertext as C ═ E (x, k), then
(·)00Taking a first element of the matrix;
step S24: second edge compute server ES2Obtaining similar weight value w according to plaintext Euclidean distance of similar pixel blockst((i, j), (a, b)), where wt((i, j), (a, b)) represents the weights of two similar pixels of the pixel in the ith row and the jth column and the pixel in the a row and the b column, and the weight value w in the ciphertext state is obtained according to the formulas (10) and (11)t((i,j),(a,b))：
In the formula, | · the luminance | |2Is the euclidean distance, h is the coefficient used to control the attenuation of the weights, Ω is the similar pixel search window; second edge compute server ES2After obtaining the weight values of the similar pixel blocks, the server ES is calculated for preventing the first edge1The weight value in the plaintext can be obtained, and the encryption processing is carried out:
wherein A is a scaling factor;
step S25: second edge compute server ES2Form a ciphertextSending the weight value under the state to a first edge computing server ES1(ii) a Then ES1Performs re-encryption, i.e. calculates equation (13), second edge calculation server ES2After key transformation, a weight value ciphertext encrypted by the master key is obtained according to the formula (14):
E(A·wt((i,j),(a,b)),k)＝k-1·diag(A·wt((i,j),(a,b)),x1,…,xg)·k； (14)
step S26: when obtaining the weight value of the encrypted main key k, the first edge computing server ES1Can encrypt the image ItPerforming non-local mean filtering processing, so as to complete the denoising operation, as shown in equation (15):
2. the method for denoising user authenticatable outsourced images based on privacy protection as claimed in claim 1, wherein the step S1 specifically comprises: the trusted third party TTP generates a master key k and assigns its private key k to the data owner COCOAssigning its private key k to an authorized user AUAUTo the first edge computing server ES1Assign its private key k'CO、
k′AUTo the second edge computing server ES2Distributing its private key
Wherein the private key satisfies: k is a radical ofCO·k′CO＝k、
k′AU·kAUK and
3. the method for denoising user authenticatable outsourcing images based on privacy protection as claimed in claim 1, wherein in step S23, the second edge computing server ES2After obtaining the distance in the ciphertext state of equation (8), performing key transformation to obtain the squared Euclidean distance in the plaintext state, as equation (9), which is the pair of ES2In other words, the approximate outline of the image can be derived by a method in which the first edge computing server ES takes into account such unsafe factors1Randomly selecting cipher text pixels to form a plurality of blocks, calculating the square of the Euclidean distance of cipher texts between the blocks by using a formula (7), using the square as noise to cover the real cipher text distance between the blocks, wherein the additional Euclidean distance only has a first edge to calculate the server ES1It is known that even ES2The distance in the plaintext can be obtained, and the true statistical distribution of the original Euclidean distance square value can not be estimated.
4. The method for denoising user authenticatable outsourced images based on privacy protection as claimed in claim 1, wherein the step S3 specifically comprises: first edge compute server ES1And (3) carrying out data similarity transformation on the denoised ciphertext image, as shown in a formula (16), and then returning to a corresponding authorized user AU:
authorized user AU uses its corresponding key kAUAnd finally decrypting to recover the required plaintext de-noised image, namely:
in the formula (I), the compound is shown in the specification,
representing an image ItThe plaintext pixel values at location (i, j) after denoising.
5. The method as claimed in claim 1, wherein when the authorized user AU is a plurality of users with different levels, each user is assigned an id number, and q levels are provided, that is, vip1,vip2,…,vipqWhen an authorized user AU submits a de-noising service request, the first edge calculation server ES1Firstly, verifying the access authority of a user, and if the user passes the verification, executing denoising service of a corresponding level; if not, ES1The service will be denied.
6. The privacy protection-based user authenticatable outsourcing image denoising method according to claim 5, wherein the authorization verification specifically comprises:
step SA: assignment of kappa to content owner CO by trusted third party TTPCOAssigning kappa to authorized user AUAU、κ′AUTo the first edge computing server ES1Dispensing
And meets the requirements that: kappaCO·κAU＝κ、
Step SB: AU submission (id) of authorized userAU,vipAU) Requesting image use right to corresponding content ownerIf the data owner CO agrees with the user, a plaintext certificate T ═ will be generated for the authorized user AU (CO, id)AU,vipAU) And using the private key kCOThe certificate is encrypted, as in equation (18), and is noted
Finally, the data owner CO respectively sends the plaintext version and the ciphertext version of the certificate to the first edge computing server ES1And an authorized user AU:
step SC: when an AU is authorized to obtain credentials
Then, use the private key κAUIt is encrypted again, i.e.:
at this time, the authorized user AU needs to perform similarity transformation once, and the specific process is as shown in equation (20):
finally, the authorized user AU will send to the first edge computing server ES1And a denoising service image use application is provided for the application;
step SD: when the first edge calculates the server ES1Using the key upon receiving a user request
To pair
Performing a decryption operation, specifically as in formula (21):
if the result of the calculation of the formula (21) is completed and the certificate
If the AU is equal to the AU, the AU is authorized to pass the user authority verification and enjoy the denoising service right; if the results are not equal, the first edge computing server ES1The current user cannot be authorized, i.e. the user cannot enjoy the denoising service, and the verification process is ended.
7. The method for denoising the user certifiable outsourcing image based on the privacy protection as claimed in claim 5, wherein if the data owner CO wants to perform outsourcing revocation certification, the method comprises the following steps:
step SA: trusted third party TTP to vCOIs distributed to the data owner CO, will
ES assigned to first edge computing server1Will be
ES assigned to the second edge compute server2(ii) a And meets the requirements that:
step SB: the data owner CO collects information of users who are not using the resource illegally and generates a corresponding certificate R ═ CO, rev, idAU,vipAU) Which isWhere rev denotes a revoke operation, the data owner CO then uses its own key vCOThe encryption certificate R is represented by formula (22):
the data owner CO converts the plaintext version R and the ciphertext version of the certificate
Respectively sent to a first edge computing server ES1And a second edge computing server ES2；
Step SC: when receiving the withdrawal request, the second edge computing server ES2To pair
And carrying out encryption again, namely:
at this time, the second edge computing server ES2A similarity transformation is required, as shown in equation (24):
second edge compute server ES2Will be provided with
Send to the first edge computing server ES1In the private key
Assisted first edge compute server ES1And executing decryption operation, specifically as (25):
by comparison
And R, a first edge computing server ES1The revocation validity can be judged, and if the revocation validity is equal, the first edge computing server ES1Performing a revocation operation on the corresponding user; if the results are not equal, the first edge computing server ES1Continue to respond to the de-noising service enjoyed on the user's certificate content.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108282333A (en) * | 2018-03-02 | 2018-07-13 | 重庆邮电大学 | Data safety sharing method under multiple edge node collaboration mode under industrial cloud environment |
| CN108390876A (en) * | 2018-02-13 | 2018-08-10 | 西安电子科技大学 | Revocation outsourcing is supported to can verify that more authorization center access control methods, Cloud Server |
| CN108921866A (en) * | 2018-07-24 | 2018-11-30 | 北京深瞐科技有限公司 | A kind of image processing method and system |
| CN110611667A (en) * | 2019-09-02 | 2019-12-24 | 深圳市赛梅斯凯科技有限公司 | Dynamic position privacy protection method and device in edge computing environment |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018026841A1 (en) * | 2016-08-01 | 2018-02-08 | Georgia Tech Research Corporation | Methods and systems for providing secure mobile edge computing ecosystems |
| CN109993153B (en) * | 2019-04-18 | 2021-05-11 | 江苏曲速教育科技有限公司 | Artificial intelligence system of going over examination papers based on edge calculation |
-
2020
- 2020-01-10 CN CN202010024855.3A patent/CN111241561B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108390876A (en) * | 2018-02-13 | 2018-08-10 | 西安电子科技大学 | Revocation outsourcing is supported to can verify that more authorization center access control methods, Cloud Server |
| CN108282333A (en) * | 2018-03-02 | 2018-07-13 | 重庆邮电大学 | Data safety sharing method under multiple edge node collaboration mode under industrial cloud environment |
| CN108921866A (en) * | 2018-07-24 | 2018-11-30 | 北京深瞐科技有限公司 | A kind of image processing method and system |
| CN110611667A (en) * | 2019-09-02 | 2019-12-24 | 深圳市赛梅斯凯科技有限公司 | Dynamic position privacy protection method and device in edge computing environment |
Non-Patent Citations (2)
| Title |
|---|
| PVIDM: Privacy-preserving verifiable shape context based image denoising and matching with efficient outsourcing in the malicious setting;Jun Zhou 等;《Computers & Security》;20191102;第1-15页 * |
| 边缘计算的架构、挑战与应用;李林哲 等;《大数据》;20190430;第3-16页 * |
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