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

Abstract

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 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 paper, which can satisfy Verifiability, Feedback, 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 cloud servers. 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 cloud servers 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 & query tokens and forward security. And extensive experiments using the real-world dataset 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.