FairCMS: Cloud Media Sharing With Fair Copyright Protection

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Author(s)
Xiao, Xiangli
Zhang, Yushu
Zhang, Leo Yu
Hua, Zhongyun
Liu, Zhe
Huang, Jiwu
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2024
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Abstract

The onerous media sharing task prompts resource-constrained media owners to seek help from a cloud platform, i.e., storing media contents in the cloud and letting the cloud do the sharing. There are three key security/privacy problems that need to be solved in the cloud media sharing scenario, including data privacy leakage and access control in the cloud, infringement on the owner’s copyright, and infringement on the user’s rights. In view of the fact that no single technique can solve the above three problems simultaneously, two cloud media sharing schemes are proposed in this article, named FairCMS-I and FairCMS-II. By cleverly utilizing the proxy re-encryption technique and the asymmetric fingerprinting (AFP) technique, FairCMS-I and FairCMS-II solve the above three problems with different privacy/efficiency tradeoffs. Among them, FairCMS-I focuses more on cloud-side efficiency while FairCMS-II focuses more on the security of the media content, which provides owners with flexibility of choice. In addition, FairCMS-I and FairCMS-II also have advantages over existing cloud media sharing efforts in terms of optional indistinguishability under chosen-plaintext attack (IND-CPA) security and high cloud-side efficiency, as well as exemption from needing a trusted third party. Furthermore, FairCMS-I and FairCMS-II allow owners to reap significant local resource savings and thus can be seen as the privacy-preserving outsourcing of AFP. Finally, the feasibility and efficiency of FairCMS-I and FairCMS-II are demonstrated by experiments.

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IEEE Transactions on Computational Social Systems

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This publication has been entered in Griffith Research Online as an advance online version.

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Media studies

Communication and media studies

Cloud computing

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Citation

Xiao, X; Zhang, Y; Zhang, LY; Hua, Z; Liu, Z; Huang, J, FairCMS: Cloud Media Sharing With Fair Copyright Protection, IEEE Transactions on Computational Social Systems, 2024

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