Privacy-preserving threshold-based image retrieval in cloud-assisted Internet of Things

Publication Type

Journal Article

Publication Date

8-2022

Abstract

Encrypted image retrieval is a promising technique for achieving data confidentiality and searchability the in cloud-assisted Internet of Things (IoT) environment. However, most of the existing top-k ranked image retrieval solutions have low retrieval efficiency, and may leak the values and orders of similarity scores to the cloud server. Hence, if a malicious server learns user background information through some improper means, then the malicious server can potentially infer user preferences and guess the most similar image content according to similarity scores. To solve the above challenges, we propose a privacy-preserving threshold-based image retrieval scheme using the convolutional neural network (CNN) model and a secure k-Nearest Neighbor (kNN) algorithm, which improves the retrieval efficiency and prevents the cloud server from learning the values and orders of similarity scores. Formal security analysis shows that our proposed scheme can resist both Ciphertext-Only-Attack (COA) and Chosen-Plaintext-Attack (CPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible for real-world datasets.

Keywords

Cloud computing, Cryptography, Data confidentiality, Encrypted image retrieval, Encryption, Feature extraction, Image retrieval, Internet of Things, Internet of Things (IoT), Privacy-preserving, Servers

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Internet of Things Journal

Volume

9

Issue

15

First Page

13598

Last Page

13611

ISSN

2327-4662

Identifier

10.1109/JIOT.2022.3142933

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/JIOT.2022.3142933

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