Confidentiality-Preserving Image Search: A Comparative Study Between Homomorphic Encryption and Distance-Preserving Randomization

Lu, Wei; Varna, Avinash L.; Wu, Min

doi:10.1109/access.2014.2307057

Cited by 69 publications

(5 citation statements)

References 37 publications

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“…Firstly, the image features used for retrieval are insufficient in expressing the content of the image, resulting in low retrieval accuracy [49][50]. Additionally, relying on a single cloud server for image retrieval increases the risk of a single point of failure [51]. Furthermore, the heavy encryption and blinding operations between multiple servers lead to inefficient search [52].…”

Section: Schemes Based On Secure Multi-party Computationmentioning

confidence: 99%

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

Zhang,

Xu,

Yan

et al. 2024

Cluster Comput

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With the growth of cloud computing, an increasing number of resourceconstrained data owners tend to store their images on the cloud, and cloud servers provide image retrieval services for users. However, this poses significant privacy leakage risks. Privacy-preserving image retrieval can prevent privacy leakage during retrieval, but existing schemes have the problem of low search accuracy. Additionally, existing solutions usually utilize a single cloud server to provide retrieval services, which suffer from problems such as a single point of failure and a greater risk of privacy leakage. This is because the cloud server is honest but curious, and it may peek at users' data out of curiosity or interest. With the proliferation of cloud service providers, image retrieval services can be supported collaboratively by multiple cloud servers. However, complex encryption techniques and frequent interactions between servers significantly degrade the performance of the overall search process. This paper proposes a lightweight three-party computation (3-PC) image retrieval strategy based on additive secret sharing in a cloud environment. Specifically, the data owner extracts the features of images using a pre-trained CNN model and constructs an index for the features using the K-means hierarchical index tree. After splitting and outsourcing the images, CNN model, and index tree to three cloud servers, the data owner remains offline. The three cloud servers process the user's query request and utilize a secure computation protocol based on secret Article Title sharing. This protocol ensures that the parameters of the neural network and the extracted query image features are hidden from the cloud servers and other attackers. Additionally, a secure feature similarity computation and distance ranking protocol is designed to avoid privacy leakage during the search process. Security analysis and experimental results demonstrate the security and retrieval performance of the scheme.

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Section: Schemes Based On Secure Multi-party Computationmentioning

confidence: 99%

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

Zhang,

Xu,

Yan

et al. 2024

Cluster Comput

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“…Guo et al [32] extracted CNN features in homomorphically encrypted domains for retrieval. Lu et al [33] compared homomorphic encryption methods with feature and index encryption methods. Homomorphic encryption can achieve the same retrieval accuracy as the plaintext domain but is computationally intensive and complex to communicate.…”

Section: Homomorphic Encryption-based Imagementioning

confidence: 99%

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

Wang,

Qin,

Xiang

et al. 2023

International Journal of Intelligent Systems

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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%.

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“…Although the work of Lu et al [20] who pioneered a different protected search technique via the use of homomorphic encryption as well as bag-of-features (BOF) enabled a greater deal of confidentiality, this method decreased the precision and efficiency of searching when compared with [16] (refer analysis in [20]). In any case, experimental test outcomes demonstrate that the plan proposed in this paper, does not release any information to untrusted servers and achieves comparable search cost and precision when compared with engines for searching images over plaintexts [17] [18].…”

Section: Related Workmentioning

confidence: 99%

EEIRI: Efficient Encrypted Image Retrieval in IoT-Cloud

2019

KSII TIIS

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One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption. However, looking through encrypted data is a difficult process. Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight. We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI. In this scheme, the images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets. To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index. Our index construction process ensures the privacy of the stored data and search requests. When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%. Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.

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Confidentiality-Preserving Image Search: A Comparative Study Between Homomorphic Encryption and Distance-Preserving Randomization

Cited by 69 publications

References 37 publications

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 Disordered Local Histograms and Vision Transformer in Cloud Computing

EEIRI: Efficient Encrypted Image Retrieval in IoT-Cloud

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