Robust Privacy-Preserving Image Sharing over Online Social Networks (OSNs)
Abstract
Sharing images online has become extremely easy and popular due to the ever-increasing adoption of mobile devices and online social networks (OSNs). The privacy issues arising from image sharing over OSNs have received significant attention in recent years. In this article, we consider the problem of designing a secure, robust, high-fidelity, storage-efficient image-sharing scheme over Facebook, a representative OSN that is widely accessed. To accomplish this goal, we first conduct an in-depth investigation on the manipulations that Facebook performs to the uploaded images. Assisted by such knowledge, we propose a DCT-domain image encryption/decryption framework that is robust against these lossy operations. As verified theoretically and experimentally, superior performance in terms of data privacy, quality of the reconstructed images, and storage cost can be achieved.
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Cites
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Only a few image encryption schemes are designed for specific application scenarios such as surveillance, IIoT, and social media.
“Only a few image encryption schemes are designed for solving security challenges in specific application scenarios: surveillance [37], IIoT (industrial Internet of Things) [29], social media [91].”
From When an attacker meets a cipher-image in 2018: A year in review (2019) · cites this paper as [91]
Cites
What Anyone Can Know: The Privacy Risks of Social Networking Sites
Rosenblum, D. (2007) · IEEE Security & Privacy · doi:10.1109/msp.2007.75 · Cited by 302 · see citing works
Give and take: a study of consumer photo-sharing culture and practice
Miller, A. D., & Edwards, W. K. (2007) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240682 · Cited by 250 · cited 1× · see citing works
“It seems that the OSN providers have not paid adequate attention to privacy protection issues, arousing much criticism from the public [16, 18, 20, 25, 42].”
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When an attacker meets a cipher-image in 2018: A year in review
Li, C., Zhang, Y., & Xie, E. Y. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102361 · Cited by 217 · see citing works
“Only a few image encryption schemes are designed for solving security challenge in specific application scenario: surveillance [37], IIoT (industrial Internet of Things) [29], social media [88].”
BackgroundMethod
- Authors
- Sun, W., Zhou, J., Zhu, S., & Tang, Y. Y.
- Affiliations
- University of Macau (MO)
- University of Electronic Science and Technology of China (CN)
- Venue
- ACM Transactions on Multimedia Computing, Communications, and Applications
- Published
- 2018-01-04
- Volume
- 14
- Issue
- 1
- Pages
- 1-22
- ISSN
- 1551-6857 (ISSN-L)
- 1551-6865
- OpenAlex
- W2782363563
- Cited by
- 32
- References
- 2
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 5 · 2021: 4 · 2020: 5 · 2019: 4 · 2018: 2
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Multimedia Computing Communications and Applications (publishedVersion)
- Funders
- Research Committee at the University of Macau (MYRG2015-00056-FST and MYRG2016-00137-FST)
- Macau Science and Technology Development Fund (FDCT/046/2014/A1 and FDCT/022/2017/A1)
- National Science Foundation of China (61402547 and 61672134)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- image sharing, social networking sites, privacy preservation, image encryption, Facebook, lossy image compression, image reconstruction quality, storage efficiency, data privacy
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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