Zhu, Lei, Song, Jiayu, Yang, Zhan, Huang, Wenti, Zhang, Chengyuan and Yu, Weiren (2021) DAP2CMH : deep adversarial privacy-preserving cross-modal hashing. Neural Processing Letters . doi:10.1007/s11063-021-10447-4 (In Press)

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Abstract

Privacy-preserving cross-modal retrieval is a significant problem in the area of multimedia analysis. As the amount of data is exploding, cross-modal data analysis and retrieval is often realized on cloud computing environment. Therefore, the privacy protection of large-scale cross-modal data has become a problem that can not be ignored. To further improve the accuracy and efficiency of privacy-preserving search, this paper proposes a novel cross-modal hashing scheme, named deep adversarial privacy-preserving cross-modal hashing (DAP2CMH). This method consists of a deep cross-modal hashing model termed DACMH, and a secure index structure called CMH2-Tree. The former is a combination of deep hashing and adversarial learning to capture intra-modal and inter-modal correlation. The latter is a hierarchical hashing index structure that can provide efficient data organization based on cross-modal hash codes. We conduct comprehensive experiments on three common used benchmarks. The results show that the proposed approach DAP2CMH outperforms the state-of-the-arts.

Item Type:	Journal Article
Divisions:	Faculty of Science > Computer Science
Journal or Publication Title:	Neural Processing Letters
Publisher:	Springer New York LLC
ISSN:	1370-4621
Official Date:	7 March 2021
Dates:	Date Event 7 March 2021 Published 2 February 2021 Accepted
DOI:	10.1007/s11063-021-10447-4
Status:	Peer Reviewed
Publication Status:	In Press
Access rights to Published version:	Restricted or Subscription Access

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