Lightweight and Controllable Privacy-Preserving Image Retrieval in Multi-User Settings

In cloud environments, privacy-preserving contentbased image retrieval (PPCBIR) enables users to retrieve images while protecting image privacy. Existing PPCBIR systems often use a single image key, which causes low efficiency and makes it difficult to achieve fine-grained access control over images. This paper proposes a lightweight and controllable privacy-preserving image retrieval in multi-user settings (named LCPIRM) to improve time efficiency and access control performance. A one-time image encryption method based on reversible embedding is proposed to balance the contradiction between complexity and security without increasing the difficulty of key management. A robust hash generation method is designed by combining piecewise mean quantization and encryption image features, which can effectively improve retrieval efficiency because the robust hashes embedded in the encrypted images can be extracted and establish inverted indexing in the cloud. When dealing with authorized encrypted images, the cloud server uses proxy re-encryption to convert the image keys embedded within themselves from the owner's public key protection to the authorized user's public key protection, achieving fine-grained access control over images in a multi-user setting. Theoretical analysis and experimental results show that LCPIRM has better performance in terms of retrieval accuracy, consumption, and search efficiency while meeting security requirements. In the real datasets Caltech256 and Caltech101, the search efficiency has increased by 74% and 58% respectively compared to the existing schemes.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Multimedia
Published
2025-01-01
DOI
https://doi.org/10.1109/tmm.2025.3586149
Primary Topic
Advanced Image and Video Retrieval Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lightweight and Controllable Privacy-Preserving Image Retrieval in Multi-User Settings

Zhuo Feng, Hongjie He, Fan Chen, Jie Bai
IEEE Transactions on Multimedia
Advanced Image and Video Retrieval Techniques
article

Lightweight and Controllable Privacy-Preserving Image Retrieval in Multi-User Settings

Zhuo Feng, Hongjie He, Fan Chen, Jie Bai
article en

Abstract

In cloud environments, privacy-preserving contentbased image retrieval (PPCBIR) enables users to retrieve images while protecting image privacy. Existing PPCBIR systems often use a single image key, which causes low efficiency and makes it difficult to achieve fine-grained access control over images. This paper proposes a lightweight and controllable privacy-preserving image retrieval in multi-user settings (named LCPIRM) to improve time efficiency and access control performance. A one-time image encryption method based on reversible embedding is proposed to balance the contradiction between complexity and security without increasing the difficulty of key management. A robust hash generation method is designed by combining piecewise mean quantization and encryption image features, which can effectively improve retrieval efficiency because the robust hashes embedded in the encrypted images can be extracted and establish inverted indexing in the cloud. When dealing with authorized encrypted images, the cloud server uses proxy re-encryption to convert the image keys embedded within themselves from the owner's public key protection to the authorized user's public key protection, achieving fine-grained access control over images in a multi-user setting. Theoretical analysis and experimental results show that LCPIRM has better performance in terms of retrieval accuracy, consumption, and search efficiency while meeting security requirements. In the real datasets Caltech256 and Caltech101, the search efficiency has increased by 74% and 58% respectively compared to the existing schemes.

IEEE Transactions on MultimediaVol. 27
Southwest Jiaotong University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 84%
Advanced Image and Video Retrieval Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Lightweight and Controllable Privacy-Preserving Image Retrieval in Multi-User Settings — Zhuo Feng, Hongjie He, et al. · IEEE Transactions on Multimedia (2025) | TGRS Research Map | TGRS