Privacy-preserving Image Retrieval Research Articles

A privacy-preserving image retrieval scheme with access control based on searchable encryption in media cloud

With the popularity of the media cloud computing industry, individuals and organizations outsource image computation and storage to the media cloud server to reduce the storage burden. Media images usually contain a large amount of private information. To prevent disclosure of privacy of the image owners, media images are encrypted before uploading to the server. However, this operation will greatly limit the utilization of the image for the user, such as content-based image retrieval. We propose an efficient similarity query algorithm with access control based on Bkd-tree in this paper, in which a searchable encryption scheme is designed for similarity image retrieval, and the encrypted image is used to extract image features by a pre-trained CNN model. The Bkd-tree is utilized to generate an index tree for the image features to speed up retrieval and make it faster than linear indexing. Finally, the security performances of the proposed scheme is analyzed and the performance of this scheme is evaluated by experiments. The results show that the security of the image content and image features can be ensured, and it has a shorter retrieval time and higher retrieval efficiency.

Verifiable Privacy-Preserving Image Retrieval in Multi-Owner Multi-User Settings

Verifiable Privacy-Preserving Image Retrieval in Multi-Owner Multi-User Settings

Privacy-preserving image retrieval based on additive secret sharing in cloud environment

Privacy-preserving image retrieval based on additive secret sharing in cloud environment

Privacy-preserving image retrieval based on additive secret sharing

Privacy-preserving image retrieval based on additive secret sharing

Segmented Hash-Based Privacy-Preserving Image Retrieval Scheme in Cloud-Assisted IoT

Segmented Hash-Based Privacy-Preserving Image Retrieval Scheme in Cloud-Assisted IoT

Privacy-Preserving Image Retrieval Based on Additive Secret Sharing

The rapid growth of digital images motivates individuals and organizations to upload their images to the cloud server. To preserve privacy, image owners would prefer to encrypt the images before uploading, but it would strongly limit the efficient usage of images. Plenty of existing schemes on privacy-preserving Content-Based Image Retrieval (PPCBIR) try to seek the balance between security and retrieval ability. However, compared to the advanced technologies in CBIR like Convolutional Neural Network (CNN), the existing PPCBIR schemes are far deficient in both accuracy and efficiency. With more cloud service providers, the collaborative secure image retrieval service provided by multiple cloud servers becomes possible. In this paper, inspired by additive secret sharing technology, we propose a series of additive secure computing protocols on numbers and matrices with better efficiency, and then show their application in PPCBIR. Specifically, we extract CNN features, decrease the dimension of features and build the index securely with the help of our protocols, which include the full process of image retrieval in the plaintext domain. The experiments and security analysis demonstrate the efficiency, accuracy, and security of our scheme.

Privacy-Preserving Image Retrieval Based on Disordered Local Histograms and Vision Transformer in Cloud Computing

Frequent data breaches in the cloud environment have seriously affected cloud subscribers and providers. Privacy-preserving image retrieval methods can improve the security of cloud image retrieval; however, existing methods have limited accuracy on dynamically updated image databases and mobile lightweight devices. In this study, we propose a privacy-preserving image retrieval method based on disordered local histograms and vision transformer in cloud computing, by designing a multiple encryption method and transformer-based feature model to better mine the local feature value of encrypted images. Specifically, the user performs different value substitution, position substitution, and color substitution on the subblocks of the image to protect the image information. The cloud server extracts the unordered local histogram from the encrypted image and generates retrievable features using transformer. Experiments show that compared with similar CNN schemes, the retrieval accuracy of this method is improved by 8.5%, and the retrieval efficiency is improved by 54.8%.

A Privacy-Preserving Image Retrieval Scheme Based on 16×16 DCT and Deep Learning

In recent years, people tend to upload images to cloud servers, which provide storage and retrieval functions. To prevent users' privacy from leaking to the server, research on cipher-image retrieval has attracted much attention. This work presents a novel encrypted image retrieval method. With this scheme, we perform encryption during the JPEG compression process by applying 16×16 DCT (Discrete Cosine Transform) for blocks' transformation, followed by coefficients distribution and 8×8 blocks' permutation. For the retrieval part, when an encrypted query image is sent by an authorized user, the server extracts its DCT histograms as features and inputs them into our trained network model, which incorporates transpose Multilayer perceptron modules ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Transpose$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$MLP$</tex-math></inline-formula> ), for retrieval. Experimental results show that our scheme, compared with related schemes, can improve the retrieval performance significantly, when ensuring compression friendliness and no feature information leakage. Moreover, our scheme enables cipher-image retrieval from multiple image owners.

A privacy-preserving content-based image retrieval method based on deep learning in cloud computing

Privacy-preserving Content-Based Image Retrieval (CBIR) method is a promising technology to achieve data confidentiality and searchability in cloud-assisted multimedia (i.e., image or video) data environment. However, inappropriate feature-preserving mechanisms and inefficient ciphertext descriptors resulted in lower performance than expected. Therefore, how to design encryption techniques with high security and how to extract effective features from ciphertext images still hinder privacy-preserving CBIR. For this goal, we propose a privacy-preserving image retrieval based on deep convolutional network features. First, a novel hybrid encryption technique is designed to encrypt images and an improved DenseNet model is fine-tuned by using the encrypted images to construct a feature extractor. The encrypted images and fine-tuning feature extractor are then uploaded to cloud server. Meanwhile, secure CBIR service is executed in the cloud server. We conduct experiments on two public benchmark datasets for performance evaluation in terms of mAP and accuracy. As demonstrated in the experimental results, the proposed method can achieve superior result compared with the existing methods, improving the performance on the two metrics by relatively 1.9% and 10%, respectively. Furthermore, the computational cost and parameters of depthwise separable convolution adopted by the improved DenseNet model are 8 to 9 times smaller than that of standard convolutions of the original DenseNet at only a small reduction in accuracy.

DVPPIR: privacy-preserving image retrieval based on DCNN and VHE

DVPPIR: privacy-preserving image retrieval based on DCNN and VHE

Privacy‐preserving image retrieval in a distributed environment

Nowadays, several image-based smart services have been widely used in our daily lives, generating many digital images. Since smart devices outsource digital images to the cloud, researchers prefer to select some desired targets from the massive images within the cloud for analysis and improve smart services. Therefore, protective image retrieval on the cloud has attained maximum concentration for privacy-preserving purposes, and the availability assurance of images on the cloud is also a crucial link. Ensuring image security and availability in the cloud environment and precisely preserving retrieval accuracy is comes as a utility-security dilemma while few existing works have explicitly addressed it. Therefore, this paper proposes privacy-preserving image retrieval in the distributed environment based on the combination of image encryption for similarity search and secret image sharing. On the basis of them, we define two-stage encryption. The first-stage encryption algorithm is introduced by modifying Wolfram's reversible cellular automata-based image encryption, which can create a set of processing images to ensure image security and retrieval accuracy. Then, the second-stage encryption algorithm is put forward based on secret image sharing to improve image security and availability. The color histogram could be extracted from the encrypted images for similarity retrieval, and the shadows could be extracted for similar image recovery. Security analysis demonstrates that image privacy and query privacy could be well protected. Moreover, the proposed work achieves more efficient performance for similarity search and similar image recovery compared with some recent works and realizes a reasonable retrieval accuracy on encrypted images for similarity search.

MSPPIR: Multi-Source Privacy-Preserving Image Retrieval in cloud computing

Content-Based Image Retrieval (CBIR) techniques have been widely researched and in service with cloud computing like Google Images. However, the images always contain rich sensitive information. In this case, privacy protection becomes a big problem as the cloud always cannot be fully trusted. Many privacy-preserving image retrieval schemes have been proposed, in which the image owner can upload the encrypted images to the cloud, and the owner himself or the authorized user can execute the secure retrieval with the help of the cloud. Nevertheless, few existing researchers notice the multi-source scene that is more practical. In this paper, we analyze the difficulties in Multi-Source Privacy-Preserving Image Retrieval (MSPPIR). Then we use the image in JPEG-format as the example, to propose a scheme called JES-MSIR, namely a novel JPEG image Encryption Scheme which is made for Multi-Source content-based Image Retrieval. JES-MSIR can support the requirements of MSPPIR, including the constant-rounds secure retrieval from multiple sources and the union of multiple sources for better retrieval services. Experiment results and security analysis on the proposed scheme show its efficiency, security, and accuracy.

A Privacy-Preserving Image Retrieval Scheme Using Secure Local Binary Pattern in Cloud Computing

The rapid growth of digital images motivates organizations and individuals to outsource image storage and computation to the cloud. However, the defenseless upload will raise the risk of privacy leakage while the simple encryption would impede the efficient usage of data. In this paper, we propose a privacy-preserving image retrieval scheme, in which the images are encrypted but similar images to a query can be efficiently retrieved from the encrypted images. Specifically, the image content is protected by big-block permutation, 3 ×3 block permutation within big-blocks, pixel permutation within 3 ×3 blocks, and polyalphabetic cipher. The use of polyalphabetic cipher improves security and causes no degradation in terms of retrieval accuracy as the substitution tables are generated by the order-preserving encryption. In this way, secure Local Binary Pattern (LBP) features can be directly extracted as the local features from the encrypted big-blocks, which is efficient as there is no communication between the cloud server and image owners to do so. The secure local LBP features are used to generate the feature vector for each image by the bag-of-words model. Finally, the similarity among the encrypted images is measured by the Manhattan distance of such feature vectors. The security analysis and experimental results demonstrate that the proposed scheme outperforms the main existing schemes in terms of security and retrieval accuracy.

TDHPPIR: An Efficient Deep Hashing Based Privacy-Preserving Image Retrieval Method

With the rapid development of multimedia techniques, mobile Internet and cloud computing, large-scale image retrieval service has become a necessity in our daily life. To overcome the challenges of privacy-preserving and data security during the image retrieval, we propose a novel deep hashing based privacy-preserving image retrieval method named TDHPPIR that can generate high quality hash codes of image and provide an efficient index structure for fast image retrieval in a security manner in cloud. A triplet Deep CNN structure model is introduced to learn deep visual representations and hash codes of images simultaneously, which can generate higher quality hash codes than the traditional way that mainly utilizes hand-crafted features. Besides, A novel hierachical bit-scalable hash codes based S-Tree, named H2S-Tree, is developed to increase the search efficiency. Comprehensive experiments on four benchmarks show that our method can combat the state-of-the-arts in both aspects of accuracy and efficiency.

SensIR: Towards privacy-sensitive image retrieval in the cloud

With the advance in content-based image retrieval and the popularity of Data-as-a-Service, enterprises can outsource their image retrieval systems on cloud platforms to reduce heavy storage, computation, and communication burdens. However, this brings many privacy problems. Although several privacy-preserving image retrieval schemes have been proposed to protect users’ privacy, they have two major drawbacks: i) the outsourced images are fully encrypted and thus cannot be used for other applications, which makes them impractical; ii) they mainly focus on traditional image retrieval systems and do not use new techniques such as convolutional neural network (CNN) to improve the accuracy. To address the above problems, we propose a novel privacy-sensitive image retrieval scheme, named SensIR, to search for similar images from an outsourced image database. In particular, we propose a privacy region detection, PRDet, to prevent private regions of images from exposing. We also propose a partial CNN (PCNN) to reduce the impact of the encrypted pseudorandom pixels. Further, we use similarity-preserving hash encoding and propose a systematic methodology to improve the accuracy of PCNN-based image retrieval when the privacy regions are large. Extensive experiments are conducted to illustrate the efficiency of privacy protection and the superior of the proposed scheme.

Privacy-Preserving Image Retrieval and Sharing in Social Multimedia Applications

Every day social multimedia applications generate millions of images. To handle such huge amount of images, an optimal solution is using the public cloud, since it has powerful storage capability. Images usually contain a wealth of sensitive information, therefore social service providers need not only to provide services such as retrieval and sharing but also to protect the privacies of the images. In this paper, we propose a privacy-preserving scheme for content-based image retrieval and sharing in social multimedia applications. First, the users extract visual features from the images, and perform locality-sensitive hashing functions on visual features to generate image profile vectors. We then model the retrieval on the images as the equality search on the image profile vectors. To enable accurate and efficient retrieval, we design the secure index structure based on cuckoo hashing, which has constant lookup time. To meet the requirements of dynamic image updating, we enrich our service with image insertion and deletion. In order to reduce the key management overhead and the access control overhead in social applications, we process keys using secret sharing techniques to enable the users holding similar images to query and decrypt images independently. Finally we implement the prototype of the proposed scheme, and perform experiments over encrypted image databases.

Secure Content-Based Image Retrieval in the Cloud With Key Confidentiality

Owing to the rapid development of cloud services and personal privacy demand, secure cloud storage services and search over encrypted datasets have become an important issue. Recently, the leaking of images such as identification and driver's licenses catches much attention. The trend towards secure computation has been widely discussed, especially asymmetric scalar-product-preserving encryption (ASPE) and homomorphic encryption (HE). Although ASPE have ability to encrypt and determine the similarity between ciphertexts efficiently, it is not a practical methodology due to its assumption that the users are fully trusted in real world and it also may have key leakage problem. Contrary to ASPE, HE can execute addition and multiplication in the encrypted domain and solve key leakage problem. Hence, in this paper, we combine the opinions of HE and ASPE to propose new privacy-preserving content-based image retrieval with key confidentiality scheme against the attacks from data owner, cloud server and users. Our privacy preserving image retrieval scheme is developed under strong threat model that is close to real world. Furthermore, to the best of our knowledge, our work is the first one that developing scheme under the assumption that all the entities involved in privacy-preserving image retrieval system are semi-trusted. Our scheme ensures the confidentiality of key and privacy of query information at the same time. In addition, we provide a lightweight verification to check whether the search query is fake or not. Finally, the experiment results show that the computation overheads and search precision are acceptable at the same time.

Privacy-preserving content-based image retrieval for mobile computing

With the increasing popularity of mobile cloud computing, image outsourcing provides a cost-effective way to support images management for smart mobile devices. However, users default that the cloud server is not trustworthy, secure image retrieval has attracted considerable interests recently to address users’ privacy concerns. Due to the limited resources such as computing power, battery lifetime, and storage capacity, it still suffers from the challenges of relieving mobile devices of excessive computation burdens, such as feature extraction and index building. In this paper, we propose a framework that supports cloud server side local-feature (scale-invariant feature transform, SIFT) extraction, index building, and image similarity scoring. We further propose a multi-index for SIFT descriptors, a secure bucket identifier computation protocol, and a secure image similarity computation protocol. In our framework, mobile users only need to encrypt and upload their images to the cloud. The later provides privacy-preserving image retrieval services without the mobile users extra interaction. Our framework greatly reduces computation overhead on the mobile user side. The security analysis and experimental evaluations demonstrate the efficiency and the scalability of our framework.

A privacy-preserving image retrieval method based on deep learning and adaptive weighted fusion

With the development of big data and cloud computing, more and more data owners store the data in cloud server. Considering privacy preserving, image data need to be encrypted before uploaded to the cloud, which will lead to inefficient image retrieval of ciphertext domain. Therefore, the challenge of encrypted image retrieval is how to improve the performance. Toward this goal, this paper proposes a privacy-preserving image retrieval method based on deep learning and adaptive weighted fusion. Firstly, extracting low-level feature EHD (edge histogram descriptor), BOW (bag of words) and high-level semantic feature of images. Secondly, reducing the dimension of 1024-dim high-level semantic feature by PCA (principal component analysis), and the three features were binarized. Then these types of features are adaptively fused. Finally, constructing a prefilter table for fusion features to improve search efficiency by locality sensitive hashing (LSH) algorithm. K-nearest neighbor (KNN) algorithm and logistic encryption method were used to protect the privacy of fused features and images, respectively. The experiments show that the proposed method can not only ensure image security but also improve the retrieval accuracy of encrypted image.

Privacy-preserving edge-assisted image retrieval and classification in IoT

Internet of Things (IoT) has drawn much attention in recent years. However, the image data captured by IoT terminal devices are closely related to users’ personal information, which are sensitive and should be protected. Though traditional privacy-preserving outsourced computing solutions such as homomorphic cryptographic primitives can support privacy-preserving computing, they consume a significant amount of computation and storage resources. Thus, it becomes a heavy burden on IoT terminal devices with limited resources. In order to reduce the resource consumption of terminal device, we propose an edge-assisted privacy-preserving outsourced computing framework for image processing, including image retrieval and classification. The edge nodes cooperate with the terminal device to protect data and support privacy-preserving computing on the semi-trusted cloud server. Under this framework, edge-assisted privacy-preserving image retrieval and classification schemes are proposed in this paper. The security analysis and performance evaluation show that the proposed schemes greatly reduce the computational, communication and storage burden of IoT terminal device while ensuring image data security.