A privacy-preserving cross-media retrieval on encrypted data in cloud computing. (March 2023)

• Record Type: Journal Article
• Authors: Wang, Zhangdong; Qin, Jiaohua; Xiang, Xuyu; Tan, Yun; Peng, Jia
• Is Part Of: Journal of information security and applications. Volume 73(2023)
• Publication Date: 2023-03
• Subjects: Cross-media -- Searchable encryption -- Convolutional neural network -- Privacy-preserving -- Cloud computing; Computer security -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.jisa.2023.103440
• Languages: English
• ISSNs: 2214-2126

Abstract

Abstract: Frequent cloud data breaches cause irreparable damage to cloud users and providers. Cross-media retrieval can better leverage the value of data, but existing cross-media retrievals are conducted on plaintext data with limited security. In this paper, we propose a privacy-preserving cross-media retrieval on encrypted data in cloud computing (PPCMR). First, the user encrypts the data and uploads it to the cloud, which enhances cloud data security. Secondly, a two-branch feature extraction network based on a convolutional neural network is designed, which is used to extract cross-media features on encrypted data in a cloud environment. And the cosine distance between cross-media features is calculated for similarity metric to solve the 'semantic gap' problem of cross-media encrypted data. Finally, the user decrypts the encrypted retrieval results from the cloud to get the cross-media plaintext results. The scheme shifts complex computing operations from the client to the cloud server, with little client-side computation. We analytically evaluate PPCMR on real-world datasets: PPCMR outperforms plaintext retrieval schemes in terms of security and supports efficient cross-media encrypted retrieval on large-scale, dynamically updated cloud databases and mobile devices (lightweight).