MLVPP
Multilevel Visual Privacy Protection via Thumbnail Preservation and Key Sharing
Bibliographic Data
| ID | 22107552 |
|---|---|
| Authors | Wenying Wen (0000-0002-3098-4640, Jiangxi University of Finance and Economics), Haigang Huang (0009-0002-6678-7515, Jiangxi University of Finance and Economics), Shuren Qi (0000-0003-0574-2313, Nanjing University of Aeronautics and Astronautics), Yushu Zhang (0000-0001-8183-8435, Nanjing University of Aeronautics and Astronautics), Yuming Fang (0000-0003-3607-2332, Jiangxi University of Finance and Economics) |
| Year | 2025 |
| Volume | 12 |
| Issue | 1 |
| Pages | 140-151 |
| Publication date | 2025-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3429356 |
| OpenAlex | W4401328226 |
| Language | EN |
| References cited | 40 |
Nowadays, shared social images often contain multiple privacy subjects, and the disclosure of these privacy subjects increases the risk of privacy violations; thus, the protection of visual privacy is particularly important. However, existing means of visual privacy protection render images unavailable and are typically protected only for images with a single privacy subject. Combining compressed sensing (CS) and 2DCS, this article proposes a multilevel visual privacy protection scheme (MLVPP) via thumbnail preservation (TPE) and key sharing (KS), which includes two stages, that is, CS-TPE multilevel encryption and KS multilevel decryption. In the first stage, we utilize 2DCS to enable the compressed sampled observations to preserve the structural similarity of the nonsensitive part and leverage CS to encrypt multiple sensitive parts of the image. The CS-processed image is then made to strike a good balance between privacy and availability through TPE. In the second stage, with the help of KS mechanism, the CS encryption keys and TPE key are shared in different combinations with the users related to the privacy subjects for meeting the MLVPP requirements. The quality of decrypted images varies for different classes of related users, e.g., unrelated users, semirelated users, and full-related users. The proposed approach preserves the visual information in the nonsensitive part of the image while providing security for the sensitive parts. Compared with related works, MLVPP reveals that the PSNR of the fully decrypted image can reach 34.04, and the SSIM is greater than 0.96 at a compression rate of 0.25. Experimental results demonstrate the superior performance of MLVPP
Computer security · Data mining · Internet privacy · Privacy Protection · Thumbnail · Visualization · Computer Science · Privacy, Security, and Data Protection · User Authentication and Security Systems · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |