4.8 Article

VerFHS: Verifiable Image Retrieval on Forward Privacy in Blockchain-Enabled IoT

期刊

IEEE INTERNET OF THINGS JOURNAL
卷 10, 期 19, 页码 17465-17478

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2023.3274641

关键词

Blockchain; dynamic setting; encrypted image retrieval; extended k-NN; forward security; permutation matrix

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In this article, the VerFHS framework is proposed to address the issue of image privacy protection in the scenario of IoT and cloud computing. By extending the secure k-NN algorithm, cleverly designing ciphertext inner product, and utilizing blockchain reward mechanism, the framework achieves verifiability, feedback, and high security. Furthermore, the VerFHSD scheme is demonstrated to achieve forward security and the effectiveness of the scheme is verified through experiments.
In the scenario of the Internet of Things (IoT) co-located with the cloud, many applications, such as face recognition, traffic monitoring, and medical diagnosis, usually outsource a large amount of generated image data to cloud servers (CSs) to reduce the burden of local storage. Many secure encrypted image retrieval schemes have been proposed to protect data privacy. However, existing work incurs storage and communication burdens, lacks verifiability of query results and has potential forward security threats. To solve these issues, we propose the VerFHS framework in this article, which can satisfy Verifiability, Feedback, and High-Security. Specifically, we first present an extended secure k -NN algorithm to protect indexes, cleverly design ciphertext inner product for similarity comparison, and use the reward mechanism of blockchain to build a monitoring and feedback mechanism for CSs. Then we demonstrate an enhanced VerFHS scheme in the dynamic setting (VerFHSD) that uses a permutation matrix to process image encryption against adaptive attacks during dynamic updates. VerFHSD prevents CSs from making search queries over newly added images via previous tokens, thereby achieving forward security. The formal security analysis shows that our schemes protect the privacy of images, indexes and query tokens, and forward security. And extensive experiments using the real-world data set demonstrate that our scheme not only has the highest search accuracy all the time, but also achieves efficient queries at the millisecond level, when compared with other advanced image retrieval schemes.

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