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Privacy-aware reversible watermarking in cloud computing environments
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Chang, Ching-Chun, Li, Chang-Tsun and Shi, Yun-Qing (2018) Privacy-aware reversible watermarking in cloud computing environments. IEEE Access, 6 . pp. 70720-70733. doi:10.1109/ACCESS.2018.2880904 ISSN 2169-3536.
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WRAP-privacy-aware-reversible-watermarking-cloud-Li-2018.pdf - Accepted Version - Requires a PDF viewer. Download (2003Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ACCESS.2018.2880904
Abstract
As an interdisciplinary research between watermarking and cryptography, privacy-aware reversible watermarking permits a party to entrust the task of embedding watermarks to a cloud service provider without compromising information privacy. The early development of schemes were primarily based upon traditional symmetric-key cryptosystems, which involve an extra implementation cost of key exchange. Although recent research attentions were drawn to schemes compatible with asymmetric-key cryptosystems, there were notable limitations in the practical aspects. In particular, the host signal must either be enciphered in a redundant way or be pre-processed prior to encryption, which would largely limit the storage efficiency and scheme universality. To relax the restrictions, we propose a novel research paradigm and devise different schemes compatible with different homomorphic cryptosystems. In the proposed schemes, the encoding function is recognised as an operation of adding noise, whereas the decoding function is perceived as a corresponding denoising process. Both online and offline contentadaptive predictors are developed to assist watermark decoding for various operational requirements. A three-way trade-off between the capacity, fidelity and reversibility is analysed mathematically and empirically. It is shown that the proposed schemes achieve the state-the-art performance.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Cloud computing, Computer security, Cryptography, Mathematical statistics | ||||||
| Journal or Publication Title: | IEEE Access | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2169-3536 | ||||||
| Official Date: | 14 November 2018 | ||||||
| Dates: |
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| Volume: | 6 | ||||||
| Page Range: | pp. 70720-70733 | ||||||
| DOI: | 10.1109/ACCESS.2018.2880904 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 23 November 2018 | ||||||
| Date of first compliant Open Access: | 23 November 2018 | ||||||
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| RIOXX Funder/Project Grant: |
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