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CN107480163B - Efficient ciphertext image retrieval method supporting privacy protection in cloud environment - Google Patents

Efficient ciphertext image retrieval method supporting privacy protection in cloud environment Download PDF

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CN107480163B
CN107480163B CN201710467097.0A CN201710467097A CN107480163B CN 107480163 B CN107480163 B CN 107480163B CN 201710467097 A CN201710467097 A CN 201710467097A CN 107480163 B CN107480163 B CN 107480163B
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cloud server
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CN107480163A (en
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陈晓峰
汪园
王剑锋
杨昌松
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Xidian University
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Abstract

The invention belongs to the technical field of image retrieval, and discloses a high-efficiency ciphertext image retrieval method supporting privacy protection in a cloud environment, which comprises the following steps: firstly, extracting a characteristic vector from an image in a database by an image owner, and encrypting the image by using an AES or RSA encryption mode; the owner establishes an index by using a hierarchical K-means algorithm based on the characteristic vector and encrypts the index by using a secure modular hash technology; and uploading the encrypted image and the index to a cloud server and executing retrieval operation. The invention can simultaneously realize the privacy protection of ciphertext image retrieval and retrieval accuracy equivalent to that of plaintext image retrieval. Furthermore, the proposed method is subjected to a detailed safety analysis and to experimental evaluation of efficiency and accuracy in different data sets. The result shows that the method can achieve the expected safety target and improve the retrieval efficiency under the condition of ensuring the retrieval accuracy.

Description

Efficient ciphertext image retrieval method supporting privacy protection in cloud environment
Technical Field
The invention belongs to the technical field of ciphertext image retrieval, and particularly relates to an efficient ciphertext image retrieval method supporting privacy protection in a cloud environment.
Background
With the advent of smart mobile devices and the popularization of the internet, more and more multimedia data are generated, and are presented in the life of people in the forms of images, videos, audios and the like every day. The more data, the more stored energy is needed, especially for multimedia data. While mobile devices are often limited by battery life, RAM storage, computing power, etc., a primary challenge for users is limited storage and processing power given that images will cost a lot of storage space. Therefore, people often choose to outsource storage and retrieval work to cloud computing. Compared with the traditional computing mode, the cloud computing can achieve better computing capability by using distributed computing and parallel computing. And cloud computing can provide customized services today and every day in the future. On the other hand, storing the image data in the cloud means that all business processing of the image data is completed in the cloud, so that the user loses the operation on the original image, and the abundant sensitive information contained in the image, such as the face of the person, the geographic position of the person, some sensitive events and the like, also poses a great threat to the privacy of the person. To reduce privacy concerns, more and more users consider encrypting images before uploading them to a cloud server. The encryption makes the image secret to the cloud server in terms of image content. However, retrieving images on encrypted databases has certain difficulties. The goal of searching in a database of dense text images is to obtain similar images without decryption. The application can carry out privacy protection on the user image in the retrieval field. Taking google search as an example, it also provides image retrieval services for individual users. Therefore, many potential users are very concerned about searching a specific image on the encrypted database, and researching an efficient ciphertext image retrieval method is not easy. Encryption is a key technology for general secure data transmission and data storage in privacy protection, but encryption has a certain barrier on similarity retrieval. Early secure information search research focused primarily on text retrieval. To detect whether the text file contains the keywords of the query, Boneh et al established a secure retrieval mechanism using public key encryption. The content of the graphics is described in text, named text-based image retrieval (TBIR), however, when the number of images is very large, it takes a lot of manpower, material resources, and financial resources to describe the images. Over the years of research, content-based image retrieval (CBIR) was proposed. Unlike text-based image retrieval, in CBIR, images use visual features to describe the content of the images, and it is typical to compare local features and global features of the images to evaluate the similarity of the images, so that the conventional searchable encryption scheme cannot meet the requirements of image retrieval. In recent years, the most commonly used encryption techniques are the order-preserving encryption algorithm and the homomorphic encryption algorithm. Lu et al encrypt the reverse order index using order preserving encryption and min-hash, respectively, but these two schemes are only applicable to the encryption of visual bag-of-words libraries of images. Hsu et al, who addresses the problem of privacy-preserving SIFT, can achieve good retrieval efficiency, but its huge computational and communication overhead is not applicable in resource-constrained devices.
In summary, the problems of the prior art are as follows: the current image retrieval method has the defects of serious resource consumption or high calculation overhead and communication overhead.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an efficient ciphertext image retrieval method supporting privacy protection in a cloud environment.
The implementation process of the invention is that the efficient ciphertext image retrieval method supporting privacy protection in the cloud environment comprises the following steps:
firstly, extracting a feature vector from an image by an image owner, and encrypting the image by using encryption technologies such as AES (advanced encryption Standard) or RSA (rivest Shamir Adleman) and the like;
secondly, establishing a retrieval index by the image owner based on the feature vector, and encrypting the index by using a secure modular hash;
uploading the encrypted image and the corresponding security index to a cloud server;
step four, the user sends a query request, the cloud server performs retrieval operation on the tree index, and a correct result is returned to the user
Further, the efficient privacy protection ciphertext image retrieval method in the cloud environment specifically comprises the following steps:
(1) the image owner owns a database containing n images, and the data owner is likely to be limited by storage resources and computing resources, and is more inclined to upload the database to a cloud for storage and perform similarity retrieval at the cloud;
(2) the image owner first extracts the L-dimensional feature vectors, f, for all images in the databasei＝{fi1,fi2,...,fiLN, the image is encrypted by using encryption technology such as AES or RSA, the image can be directly encrypted as ordinary data, and the encrypted image is represented as { C ═ C1,C2,...Cn}；
(3) The image owner uses the feature vectors to establish tree retrieval indexes, each node of each layer utilizes a K-means clustering algorithm to cluster the feature vectors of the nodes into T classes, each class is a child node of the node, and the operation is executed recursively until the number of child nodes contained in the last node is less than T. Finally, all the nodes are encrypted, each leaf node is associated with one encrypted image, and the CTRe is output;
(4) and uploading the encrypted image and the corresponding security index to a cloud server, sending a retrieval request to the cloud server when a user needs to retrieve, returning a correct retrieval result to a data owner by the cloud server, decrypting and returning the correct retrieval result to the client.
Further, the efficient ciphertext image retrieval method supporting privacy protection in the cloud environment further comprises the following steps:
inputting security parameter lambda, outputting Key SK, encrypting image content, randomly generating a matrix M with L multiplied by J dimension by an image owner, randomly generating a bit string S (M, S) for encrypting a Key of a feature vector by each line of data elements in the matrix according to Gaussian distribution, and secretly storing the Key Key (SK, (M, S)) by the image owner;
GenIndex({f1,...,fnin order to accelerate the retrieval process, based on the characteristic vector, a hierarchical K-means clustering algorithm is used for establishing a tree index; taking all the feature vectors as input, and outputting the feature vectors as Tree; in each layer, dividing all the feature vectors into T classes by using a K-means clustering algorithm, wherein each class is used as a child node of the node, and recursively executing the K-means algorithm until the last leaf node contains less than T features; each leaf node is associated with an encrypted image, and finally, a security index CTRee is output;
the GenCrypt (Key) takes a main Key Key as input and comprises two specific processes, wherein a Key SK is used for image encryption, and the other Key (M, S) is used for encrypting Tree and outputting CTRee;
DecImg(CkSK) cloud Server returns the first kThe relevant encrypted image is sent to the image owner, and the image owner decrypts the encrypted image by using the SK; the data owner provides authentication for the user upon receipt of the user request, and the user receives the decrypted image from the data owner after the authentication is passed.
Further, the encrypted image content is kept secret from the cloud server, and the data owner encrypts all images { I } with an encryption algorithm1,I2,...,InIs { C }1,C2,...,CnAnd leaf nodes of the CTree are associated with the encrypted image.
Further, the key (M, S) used to encrypt the Tree output ctre comprises: extracting feature vectors f of all images by data owneri＝{fi1,fi2,..., f iL1, 2.., n, the matrix M and the random string S are keys used to encrypt the feature vectors; encrypting the Tree index Tree into CTRee, and for each vector of CTRee, encrypting the way as follows:
Figure BDA0001325174340000041
each node of the CTRee stores the corresponding hash value, and the leaf nodes of the CTRee correspond to the corresponding encrypted images;
GenSearch(Qq) User extracts feature vector f for retrieval imageqAnd encrypting the feature vector by using the same secret key to obtain Qq：
Figure BDA0001325174340000042
Uploading the encrypted query features to a cloud server, and receiving QqThen, the retrieval process is started from the top end of the CTRee and executed to a leaf node; at each layer of the CTree, the cloud server finds the minimum point along this path by computing the hamming distance between the node and the query vector, until the last layer, when Q isqWhen the leaf node is reached, the cloud returns a list of related images corresponding to the encrypted images; all queried images are returned to the user.
The invention further aims to provide an efficient ciphertext image retrieval method supporting privacy protection in the cloud environment. The efficient ciphertext image retrieval system supporting the privacy protection in the cloud environment comprises:
the image owner module owns the image database and outsources the image database to the cloud server in a ciphertext mode; generating a safe query index and uploading the query index to the cloud, and once receiving a request of a user, an image owner decrypts the query index by using the key SK to recover an encrypted picture;
the user module, user, refers to an authorized user, where the authorization process is considered to have been completed. In the image retrieval system, a user sends a query request to obtain similar retrieval images;
the cloud server module has distributed computing capacity and strong storage capacity, is used for storing the encrypted images, executes retrieval operation on the encrypted images and returns the retrieved related images to the user.
The invention also aims to provide an efficient ciphertext image retrieval method supporting privacy protection applied to the cloud environment.
The invention has the advantages and positive effects that: according to the invention, a resource-limited device is considered, an efficient ciphertext image retrieval scheme supporting privacy protection under cloud computing is provided, and the scheme has light-weight computing overhead and is suitable for a resource-limited user. In the whole scheme, a tree index is created by using a K-means algorithm, and then the index is encrypted by using a secure modulo hash. Because of the use of tree structures and the calculation of hamming distances, the time overhead at the retrieval stage is significantly less than for linear retrieval. The security modular hashing technology is used for replacing homomorphic encryption to protect the privacy of the image, and the calculation cost and the communication cost are greatly reduced. In addition, through detailed experimental analysis, the scheme can obtain retrieval accuracy equivalent to that of retrieval in the clear text, so all the above shows that the scheme is suitable for practical application. 1. To achieve distance computation under ciphertext that approximates that in the plaintext state, secure modulo hashing is used. In this way, the privacy of the feature vector can be ensured. In addition, the method can ensure that the retrieval accuracy of the ciphertext image is similar to that of the plaintext retrieval.
2. The scheme provided by the invention is more effective. The tree index is built by fully utilizing the K-means algorithm, the retrieval speed can be accelerated, and the retrieval accuracy is not seriously reduced.
3. Because of the use of the secure modular hashing technique, the scheme provided by the invention only needs one round of communication overhead and does not need additional other calculation. In addition, since the hamming distance is calculated during the search, a part of the retrieval overhead can be reduced.
Drawings
Fig. 1 is a flowchart of an efficient ciphertext image retrieval method supporting privacy protection in a cloud environment according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a system framework provided by an embodiment of the present invention.
Fig. 3 is a schematic diagram of a process for building a tree structure according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of performing retrieval on an index by using an encrypted query according to an embodiment of the present invention.
FIG. 5 shows a diagram of d according to an embodiment of the present inventionQ(fi,fq) The value of (a) is | | | fi-fqA functional diagram of | l.
Fig. 6 is a schematic diagram of time overhead for encrypting a feature vector according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a time overhead test of retrieval according to an embodiment of the present invention.
FIG. 8 is a diagram illustrating a relationship between different recals and precision according to an embodiment of the present invention.
Fig. 9 is an experimental schematic diagram of the retrieval accuracy in different dimensions provided by the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
With the rapid development of cloud computing and big data, outsourcing large-scale data to a cloud server has become a hot trend. While outsourcing can mitigate data maintenance overhead at the user end, it comes with new security challenges where image privacy is of great research interest. Therefore, ciphertext image retrieval supporting privacy protection becomes a new research hotspot in academia. The invention provides a new efficient ciphertext image retrieval scheme, and the K-means clustering algorithm and the secure modular hashing technology are comprehensively used, so that the privacy protection of ciphertext image retrieval can be realized simultaneously, and the retrieval accuracy equivalent to that of plaintext image retrieval is obtained. Furthermore, the proposed method is subjected to a detailed safety analysis and to experimental evaluation of efficiency and accuracy in different actual data sets. The result shows that the method can achieve the expected safety target and improve the retrieval efficiency under the condition of ensuring the retrieval accuracy.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, the efficient ciphertext image retrieval method supporting privacy protection in a cloud environment according to the embodiment of the present invention includes the following steps:
s101: extracting a characteristic vector from the image, and encrypting the image by using an AES or RSA encryption mode;
s102: the data owner establishes a retrieval index by using the feature vector;
s103: uploading the encrypted image and the index to a cloud server;
s104: and the user sends a query request, the cloud server performs retrieval operation on the tree index, and a correct result is returned to the user.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
1. System model
The invention mainly comprises three parts: a schematic diagram of a specific system model of an image owner, a user and a cloud server is shown in fig. 2.
And the image owner module is used for owning an image database and outsourcing the image database to the cloud server in a ciphertext mode. In addition, the image owner generates a secure index and uploads it to the cloud. Upon receiving the user's request, the image owner will decrypt with the private key to recover the encrypted picture.
The user module, user, refers to an authorized user, where authorization operations are considered to have been performed. Additionally, users may be limited by storage resources and computing resources. In an image retrieval system, a user sends a query request to obtain similar retrieval images.
The cloud server module is provided with distributed computing capability and strong storage capability, and is mainly used for storing the encrypted images, executing retrieval operation on the encrypted images and returning the related images to the user.
The cloud server of the invention can honestly execute the specified protocol and return a correct result to the user, but the cloud server can analyze the encrypted image and the security index stored on the cloud server to obtain related plaintext information. In addition, the invention considers that the user and the data owner are credible and can not be in collusion with the cloud server.
The efficient ciphertext image retrieval method supporting privacy protection under cloud computing mainly achieves the following aims:
privacy. First it is ensured that the image database and the corresponding feature vectors are kept secret. That is, the server does not get any plaintext information without decryption.
Efficiency. The client can support effective calculation, and the calculation overhead and the communication overhead of the client are enabled to be as small as possible under the condition that the client is possibly limited by resources.
And (4) accuracy. Since the similarity evaluation is performed in the ciphertext, it is necessary to ensure that the search result obtained in the ciphertext is similar to the search result obtained in the plaintext.
2. Search method of the present invention
Image ownerHaving a database containing n images, and the data owner may be limited by storage and computing resources, may prefer to upload the database to the cloud. To be able to perform the retrieval function in the cloud, the owner first extracts the L-dimensional feature vector, f, from the imagei＝{fi1,fi2,...,fiLN, the image is encrypted by using an advanced encryption mode, such as an AES encryption algorithm and an RSA encryption algorithm, and the image is directly encrypted as common data. The encrypted image is denoted as { C1,C2,...Cn}. The data owner then builds a search index using the feature vectors. The index tree is established by using a K-means algorithm, all nodes are encrypted, each leaf node is associated with one encrypted image, and the CTRee is output. And finally, uploading the encrypted image and the index to a cloud server. Therefore, when the user needs to search, only the search request needs to be sent to the cloud server, and the cloud server returns the correct search result to the data owner, and the correct search result is decrypted by the data owner and returned to the client.
2.1 the algorithm contained in the present invention is explained in detail:
GenKey (lambda) inputs a security parameter lambda and outputs a key SK which is used for encrypting image content. On the other hand, the data owner randomly generates an L J matrix M whose elements satisfy the Gaussian distribution, and a random bit string S whose values satisfy the uniform distribution of [0, k ]. The matrix M and the random bit string S are keys used to encrypt the feature vector, and finally the Key is (SK, (M, S)) kept secret by the data owner.
GenIndex({f1,...,fnAnd the index is a meaningful research hotspot in the field of image retrieval, and the establishment of the index can accelerate the retrieval process. But there is no more efficient indexing scheme than linear retrieval. In image retrieval, the reverse order index is a more common index structure, but the reverse order index is only suitable for visual word bags and has great limitation. In order not to greatly reduce the retrieval accuracy, a hierarchical K-means clustering algorithm is used for establishing the tree index. In this algorithm, all feature vectors are input and the output is Tree. At each of the layers of the stack, the layers are,and dividing all the feature vectors into T classes by using a K-means algorithm, and recursively executing the K-means algorithm until the last leaf node contains less than T features. Each leaf node is associated with an encrypted image. The detailed procedure will be described in algorithm 1. Num in Algorithm 1tIndicating the number of feature vectors in class t. In addition, the vector nodes in the same class are from the same parent node, denoted as fhThe value is the center value of the class. Fig. 3 is a process of building a tree structure.
Figure BDA0001325174340000091
GenCrypt (Key) takes a main Key Key as input, and the algorithm comprises two specific processes, wherein a Key SK is used for encrypting images, and the other Key (M, S) is used for encrypting Tree output CTRee, which specifically comprises the following steps:
1. image encryption: to ensure that the content of the image is kept secret from the cloud server, the data owner encrypts all images I with an advanced encryption algorithm1,I2,...,InIs { C }1,C2,...,CnAnd the leaf node of the CTRee is associated with the encrypted image.
2. Encryption of the feature vector: extracting feature vectors f of all images by data owneri＝{fi1,fi2,..., f iL1, 2.., n, the matrix M and the random string S are the keys used to encrypt the feature vectors. Encrypting the Tree index Tree into CTRee, and for each vector of CTRee, encrypting the way as follows:
Figure BDA0001325174340000101
each node of the CTree stores its corresponding hash value and its leaf nodes correspond to their respective encrypted images.
GenSearch(Qq): user extraction of feature vector f for retrieval imageqAnd encrypting the feature vector by using the same secret key to obtain Qq：
Figure BDA0001325174340000102
Upload the encrypted query features to the cloud, as shown in FIG. 4, with Q receivedqThereafter, the retrieval process starts at the top of ctre and proceeds to the leaf node. At each layer of the CTree, the cloud server finds the minimum point by the hamming distance between the compute node and the query vector and goes along this path until the last layer, when Q isqReaching the leaf node, the cloud will return a list of the most relevant images, corresponding to the encrypted images. All the inquired images are returned to the user, and fig. 4 shows a schematic diagram of the whole inquiry process, which is described in detail in algorithm 2.
Node in algorithm 2hNode, representing the h-th level in the CTReehNum represents the number of child nodes of a node, QhaIs the value of the node.
Figure BDA0001325174340000111
DecImg(CkSK) the cloud server returns the first k relevant encrypted images to the image owner, who decrypts them using SK. Upon receipt of the user request, the data owner will provide authentication of the user, and upon passing the authentication, the user receives the decrypted image from the data owner.
2.2 analysis of correctness
By pair dQ(fi,fq) And fi-fqThe accuracy of the algorithm is explained by analyzing the relation between | |, and the equation (1) can obtain the f-fq、fi∈RN，P(Qk(fi)＝Qk(fq) Has the following relationship:
Figure BDA0001325174340000112
the cloud server calculates the following formula instead
Figure BDA0001325174340000113
When k is 2, dQ(fi,fq) And fi、fqHamming distances between them are equal, fig. 5 shows dQ(fi,fq) The value of (a) is | | | fi-fqA function of.
When the distance between two vectors is close enough, dQ(fi,fq) Can replace fi-fqI, when k is 2, it is equivalent to the calculation of hamming distance instead of euclidean distance, but when fi-fqIf | | is greater than a value r, the two vectors will be indistinguishable, so the present invention uses a color histogram to represent the feature vectors of the image, each value being normalized to meet the requirement of distance calculation.
The safety and efficiency of the present invention will be described in detail in connection with the analysis below.
1.1 safety assay
Privacy of image content: all images are encrypted using more advanced encryption before being outsourced to the cloud. Under the known ciphertext model, the method is confidential for the cloud server.
Privacy of feature vectors: under the known plaintext model, the query vector and the index tree have been encrypted using secure modulo hashing techniques. Without knowing the keys M and S, the server does not have enough capacity to recover the feature vectors in ctre. And M and S are independently randomly generated, the generation space of M and S is large enough, and P ((M, S) ═ M ', S') will be close to 0, assuming a curious server guesses one of M 'and S'.
On the other hand, when the distance between two vectors is greater than a constant value r, the hash value between the two vectors is indistinguishable. Qk(fi) And Qk(fq) Mutual information between is
Figure BDA0001325174340000121
When fi-fjThe mutual information reduces the exponent to 0. alsoThat is, when k is 2 and the euclidean distance between vectors is greater than 5 δ, the boundary value of the mutual information will be 10-50I.e. from the vector fqMiddle leakage fiWill require 1050A hash string.
1.2 efficiency analysis
The computational efficiency of the present invention was analyzed. It is noted from table 1 that J/L ═ t is defined in the encryption time, where L denotes the dimension of the feature vector, J denotes the key length (length after encryption), and h denotes the secure modulo hash operation, and the encryption time of the feature vector depends mainly on t, and the number of images in the entire image set is a decisive factor.
In the search time, the calculation in plaintext is performed by using euclidean distance calculation, the calculation in ciphertext is performed by using hamming distance, and D and H are used to represent euclidean distance calculation and hamming distance calculation, respectively, during the search process, all searches are performed in a tree structure, and the whole search overhead is represented in table 1.
TABLE 1
Figure BDA0001325174340000131
The effect of the present invention will be described in detail below with reference to performance evaluation.
The search experiment was performed on the basis of two image databases. One of them is from the Corel image library, which contains 1000 color images, the images can be divided into 10 classes according to the content, each class has 100 pictures: africa, beach, building, bus, dinosaur, elephant, flower, horse, mountain, and food. The size of the image is 256 × 384 or 384 × 256. Another is the well-known INRIA landmark image database, which mainly contains the landscape images they take during vacation, and is mainly used to evaluate the search effect, and 259 images are randomly selected from the image set for testing. All experimental hardware configurations were Intel (R) core (TM) i 3-21203.3 GHz CPU, 4G memory. The development language mainly uses C + + and MATLAB. In the experimental process, n represents the number of images, L represents the dimension of the feature vector, and J represents the length of the hash. For sufficient comparison, the feature vector is a color histogram of colors, saturation, and values in the color space, and values of different dimensions are extracted to evaluate the solution. In addition, J is also another key factor related to the accuracy of the search.
Encryption time: after extracting the feature vectors from all the images, the feature vectors are encrypted to protect the privacy of the images. As shown in fig. 6(a), it can be seen that the encryption time is determined by the number of images in the database. Furthermore, the ratio of the size of the defined key and the dimension of the feature is variable, and as an argument, the encryption times in different dimensions are compared. Fig. 6(b) shows a curve in which the encryption time linearly increases with an increase in the ratio when the number of images is fixed at 1000. This is because each encryption requires the calculation of (L × J) products, and when n and L are fixed, the encryption time is proportional to J. Different dimensions require different encryption times, and the larger the dimension, the more time overhead.
And (3) retrieval time: in the retrieval stage, first, the linear search and the tree index retrieval are tested, and as can be seen from fig. 7(a), the linear search cost increases linearly with the increase of the number of images, and when the increase of the number of images is accelerated, the search cost increases rapidly. Furthermore, the retrieval overhead of tree indexes is significantly less than linear searches.
The search costs in plaintext and ciphertext were tested separately. In the plaintext, the query feature vector first reaches the root node, the distances between the query feature vector and all child nodes of the root in Euclidean space are calculated and sorted according to the size, and then the smallest one is selected to be along the path. And repeating the steps until the leaf node is reached and the most relevant image index is returned to the user. Unlike retrieval in plaintext, in ciphertext, the cloud server calculates the hamming distance instead of the euclidean distance until the last layer. As can be seen from fig. 7(b), the secure indexing scheme allows the server to perform a retrieval operation, and the retrieval efficiency in ciphertext is comparable to the speed in plaintext. When J/L is 2, the search time in plain text and cipher text is almost the same.
The search accuracy is as follows: in the present invention, two databases are used to evaluate the search accuracy of the proposed solution. One aspect is to evaluate with precision-call using the Coral database, to evaluate the encrypted results, take each image as a query, obtain the accuracy of all the query images, and calculate the average accuracy of 1000 images. FIG. 8 reflects the relationship between different recals and precision. As can be seen from fig. 8, when J/L is 2, the retrieval accuracy of the ciphertext image is almost the same as that in the plaintext, and when J/L is 4, the retrieval accuracy of the ciphertext image is less than that in the plaintext. By encrypting the query request using a key other than the encrypted index, it can be seen that the retrieval accuracy is significantly lower than otherwise, and therefore it can be seen as a random selection of images from a database of images. Although the cloud server performs data retrieval through its own image, it does not get any useful information without an accurate key.
In the INRIA Holiday picture database, the retrieval accuracy is represented by MAP. In the search experiment, each image was used as a query image and the average result was obtained. Furthermore, the same search experiment was performed in different dimensions. As can be taken from fig. 9, after encryption, MAP decreases as the key dimension decreases. It is clear that when J/L falls below 4, MAP will drop sharply with decreasing ratio.
In summary, the present invention can consider that the accuracy of image retrieval is closely related to the image descriptor, the feature matrix size, and the image database.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (6)

1.一种云环境下支持隐私保护的高效密文图像检索方法，其特征在于，云环境下支持隐私保护的高效密文图像检索方法在云环境下支持图像的隐私保护并实现高效的密文图像检索，所述方法包括以下步骤：1. an efficient ciphertext image retrieval method that supports privacy protection under a cloud environment, is characterized in that, the efficient ciphertext image retrieval method that supports privacy protection under cloud environment supports the privacy protection of images and realizes efficient ciphertext under cloud environment. Image retrieval, the method includes the following steps: 步骤一，图像拥有者对所拥有的所有图像提取特征向量，使用AES或RSA加密算法对图像内容进行加密；Step 1, the image owner extracts feature vectors from all images owned, and uses AES or RSA encryption algorithm to encrypt the image content; 步骤二，图像拥有者基于图像特征向量，使用K-means算法建立树形检索索引，并对索引中的全部向量使用安全模哈希进行加密，输出安全索引；Step 2, the image owner uses the K-means algorithm to establish a tree-shaped retrieval index based on the image feature vector, encrypts all the vectors in the index using a secure modulo hash, and outputs the secure index; 步骤三，将加密后的图像和相对应的安全索引上传给云服务器，由云服务器保存；Step 3, upload the encrypted image and the corresponding security index to the cloud server, and the cloud server saves it; 步骤四，用户生成查询请求发送给云服务器，云服务器在密文图像下执行检索操作，并将正确的相似性图像返回给用户；Step 4, the user generates a query request and sends it to the cloud server, and the cloud server performs a retrieval operation under the ciphertext image, and returns the correct similarity image to the user; 所述云环境下支持隐私保护的高效密文图像检索方法具体包括以下步骤：The efficient ciphertext image retrieval method supporting privacy protection in the cloud environment specifically includes the following steps: (1)图像拥有者拥有包含n幅图像的数据库，图像拥有者可能受到存储资源和计算资源的限制，将图像数据库上传到云端进行存储；(1) The image owner has a database containing n images, and the image owner may be limited by storage resources and computing resources, and upload the image database to the cloud for storage; (2)图像拥有者首先对图像提取L-维特征向量，fi＝{fi1,fi2,...,fiL}，i＝1,2,...,n，使用AES或RSA加密算法对图像内容进行加密，将图像看成普通数据直接进行加密，加密后的图像表示为{C1,C2,...Cn}；(2) The image owner first extracts the L-dimensional feature vector for the image, f i ={f i1 ,f i2 ,...,f iL }, i=1,2,...,n, using AES or RSA The encryption algorithm encrypts the image content, and directly encrypts the image as ordinary data. The encrypted image is expressed as {C 1 ,C 2 ,...C n }; (3)图像拥有者基于特征向量建立树形检索索引，从根节点到叶子节点，树形结构的每一层都使用K-means聚类算法将特征向量分成T类，每一类构成树的一个节点，每个节点都重复上述步骤直到节点中所包含的特征向量少于T个；将建立好的树结构的所有节点用安全模哈希进行加密隐藏，且每个叶子结点和一个加密图像相关联，最终输出CTree；(3) The image owner establishes a tree retrieval index based on the feature vector. From the root node to the leaf node, each layer of the tree structure uses the K-means clustering algorithm to divide the feature vector into T categories, and each category constitutes a tree. A node, each node repeats the above steps until the node contains less than T eigenvectors; all nodes in the established tree structure are encrypted and hidden with a secure modulo hash, and each leaf node and a The encrypted images are associated, and the CTree is finally output; (4)将加密后的图像和相对应的安全索引上传给云服务器，当用户需要检索时，将检索请求发送给云服务器，云服务器将正确的检索结果返回给数据拥有者，由图像拥有者用加密密钥对返回密文图像进行解密并返回给用户。(4) Upload the encrypted image and the corresponding security index to the cloud server. When the user needs to retrieve, send the retrieval request to the cloud server, and the cloud server will return the correct retrieval result to the data owner. Decrypt the returned ciphertext image with the encryption key and return it to the user. 2.如权利要求1所述的云环境下支持隐私保护的高效密文图像检索方法，其特征在于，所述云环境下支持隐私保护的高效密文图像检索方法进一步包括：2. the efficient ciphertext image retrieval method supporting privacy protection under cloud environment as claimed in claim 1, is characterized in that, the efficient ciphertext image retrieval method supporting privacy protection under described cloud environment further comprises: GenKey(λ):输入安全参数λ，输出密钥SK，用来对图像内容进行加密，由图像拥有者随机生成一个L×J维的矩阵M，矩阵中的每一列都服从高斯分布，随机生成一个比特串S，(M,S)用来加密特征向量的密钥，密钥Key＝(SK,(M,S))由图像拥有者秘密保存；GenKey(λ): Input security parameter λ, output key SK, used to encrypt the image content, an L×J-dimensional matrix M is randomly generated by the image owner, and each column in the matrix obeys the Gaussian distribution and is randomly generated A bit string S, (M, S) is used to encrypt the key of the feature vector, and the key Key=(SK, (M, S)) is secretly stored by the image owner; GenIndex({f1,...,fn}):为加快检索的进程，使用分层K-means聚类算法建立树形索引；所有特征向量作为输入，输出为Tree；在树的每一层，利用K-means算法将所有的特征向量分成T类，递归执行K-means算法，直到最后的节点包含的特征向量少于T个；每一个叶子结点关联一个加密图像；GenIndex({f 1 ,...,f n }): In order to speed up the retrieval process, the hierarchical K-means clustering algorithm is used to establish a tree index; all feature vectors are used as input, and the output is Tree; Layer, using the K-means algorithm to divide all feature vectors into T classes, and recursively execute the K-means algorithm until the last node contains less than T feature vectors; each leaf node is associated with an encrypted image; GenCrypt(Key):以主密钥Key作为输入，包含两个具体过程，其中密钥SK用来图像加密，另一个密钥(M,S)用来加密Tree输出CTree；GenCrypt(Key): takes the master key Key as input, including two specific processes, in which the key SK is used for image encryption, and the other key (M, S) is used to encrypt the Tree output CTree; DecImg(Ck,SK):云服务器返回前k个相关的加密图像给图像拥有者，图像拥有者使用SK对返回密文图像进行解密；一旦收到用户请求，数据拥有者将提供对用户的认证，认证通过后，用户从数据拥有者收到解密后的图像。DecImg(C k ,SK): The cloud server returns the first k related encrypted images to the image owner, and the image owner uses SK to decrypt the returned ciphertext image; once the user request is received, the data owner will provide the user's Authentication. After the authentication is passed, the user receives the decrypted image from the data owner. 3.如权利要求2所述的云环境下支持隐私保护的高效密文图像检索方法，其特征在于，所述图像加密的内容对云服务器保密，数据拥有者用加密算法加密所有的图像{I1,I2,...,In}为{C1,C2,...,Cn}，CTree的叶子节点和加密后的图像关联。3. the efficient ciphertext image retrieval method supporting privacy protection under cloud environment as claimed in claim 2 is characterized in that, the content of described image encryption is kept secret to cloud server, and data owner encrypts all images with encryption algorithm. 1 ,I 2 ,...,In } is {C 1 ,C 2 ,...,C n }, and the leaf nodes of CTree are associated with the encrypted image. 4.如权利要求2所述的云环境下支持隐私保护的高效密文图像检索方法，其特征在于，所述密钥(M,S)用来加密Tree输出CTree包括：数据拥有者提取所有图像的特征向量fi＝{fi1,fi2,...,fiL}，i＝1,2,...,n，矩阵M和随机串S是用来加密特征向量的密钥；把树形索引Tree加密生成CTree，对于CTree的每一个向量，加密方式如下：4. the efficient ciphertext image retrieval method supporting privacy protection under cloud environment as claimed in claim 2, is characterized in that, described key (M, S) is used for encrypting Tree output CTree and comprises: data owner extracts all images The eigenvectors f i ={f i1 ,f i2 ,...,f iL }, i=1,2,...,n, the matrix M and the random string S are the keys used to encrypt the eigenvectors; put The tree index Tree is encrypted to generate CTree. For each vector of CTree, the encryption method is as follows:
Figure FDA0002208565880000031
Figure FDA0002208565880000031
 CTree的每个节点存储着其对应的哈希值，且其叶子节点对应着其相应的加密图像；Each node of CTree stores its corresponding hash value, and its leaf node corresponds to its corresponding encrypted image; GenSearch(Qq):用户对检索图像提取特征向量fq，使用相同的密钥对特征向量进行加密，得到Qq：GenSearch(Q q ): The user extracts the feature vector f q from the retrieved image, and encrypts the feature vector with the same key to obtain Q q :
Figure FDA0002208565880000032
Figure FDA0002208565880000032
 将加密后的查询向量上传给云，云服务器收到Qq之后，执行检索操作；检索过程从CTree根节点开始，执行到叶子节点；在CTree的每一层，云服务器通过计算树节点和查询向量之间的汉明距离找到距离最小的节点，沿着这条路径直到最后一层，当Qq到达叶子节点，云服务器将返回相关的图像索引，与加密的图像相对应；最后将所查询到的图像返回给用户。Upload the encrypted query vector to the cloud, and after the cloud server receives the Q q , execute the retrieval operation; the retrieval process starts from the root node of the CTree and executes to the leaf nodes; at each layer of the CTree, the cloud server calculates the tree node and query The Hamming distance between vectors finds the node with the smallest distance, and follows this path until the last layer. When Q q reaches the leaf node, the cloud server will return the relevant image index, which corresponds to the encrypted image; The resulting image is returned to the user. 5.一种如权利要求1所述云环境下支持隐私保护的高效密文图像检索方法的云环境下支持隐私保护的高效密文图像检索系统，其特征在于，所述云环境下支持隐私保护的高效密文图像检索系统包括：5. a high-efficiency ciphertext image retrieval system supporting privacy protection under the cloud environment of the high-efficiency ciphertext image retrieval method supporting privacy protection under the cloud environment as claimed in claim 1, is characterized in that, supporting privacy protection under the cloud environment The efficient ciphertext image retrieval system includes: 图像拥有者模块，拥有图像数据库，并以密文的形式外包给云服务器；基于图像特征向量和安全模哈希生成安全的树形查询索引并上传给云，一旦收到用户的请求，数据拥有者将使用私钥SK解密恢复出被加密的图片；The image owner module owns the image database and outsources it to the cloud server in the form of ciphertext; generates a secure tree query index based on the image feature vector and security modulo hash and uploads it to the cloud. Once the user's request is received, the data has The user will use the private key SK to decrypt and recover the encrypted image; 用户模块，用户指的是已授权用户；指资源受限的客户端，在图像检索系统中，用户发送一个查询请求并获得相似的检索图像；User module, the user refers to the authorized user; refers to the client with limited resources, in the image retrieval system, the user sends a query request and obtains similar retrieval images; 云服务器模块，云服务器提供分布式计算能力和强大的存储能力，用于对加密的图像进行存储，并在用户发送查询请求时在加密的图像上执行检索操作，将相似图像返回给用户。Cloud server module, the cloud server provides distributed computing capability and powerful storage capability, is used to store encrypted images, and performs retrieval operations on the encrypted images when the user sends a query request, and returns similar images to the user. 6.一种应用权利要求1～4任意一项所述云环境下支持隐私保护的高效密文图像检索方法的云服务器。6. A cloud server applying the high-efficiency ciphertext image retrieval method supporting privacy protection in a cloud environment according to any one of claims 1 to 4.
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