Go-Sharing: A Blockchain-Based Privacy-Preserving Framework for Cross-Social Network Photo Sharing
Go-Sharing: A Blockchain-Based Privacy-Preserving Framework for Cross-Social Network Photo Sharing
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DOI：
10.1109/tdsc.2022.3208934
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发表时间：
2023-09
影响因子：
7.3
通讯作者：
Ming Zhang;Zhe Sun;Hui Li;Ben Niu;Fenghua Li;Zixu Zhang;Yuhang Xie;Chunhao Zheng
中科院分区：
文献类型：
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作者：
Ming Zhang;Zhe Sun;Hui Li;Ben Niu;Fenghua Li;Zixu Zhang;Yuhang Xie;Chunhao Zheng
社交媒体的发展导致了在在线社交网络平台（SNP）上发布日常照片的趋势。在线照片的隐私通常受到安全机制的精心保护。然而，当有人将照片传播到其他平台时，这些机制将失去效力。在本文中，我们提出了Go-sharing，这是一个基于区块链的隐私保护框架，为跨SNP照片共享提供了强大的传播控制。与在不信任彼此的集中式服务器中单独运行的安全机制相反，我们的框架通过精心设计的基于智能合约的协议在照片传播控制上实现了一致的共识。我们使用这些协议为每个图像创建无平台传播树，为用户提供完整的共享控制和隐私保护。考虑到在跨SNP共享中转发者和转发者之间可能存在的隐私冲突，本文设计了一种动态隐私策略生成算法，在不侵犯转发者隐私的前提下，最大限度地提高转发者的灵活性。此外，Go-sharing还提供了强大的照片所有权识别机制，以避免非法转载。它在两阶段可分离的深度学习过程中引入了随机噪声黑盒，以提高对不可预测操作的鲁棒性。通过广泛的真实世界的模拟，结果证明了跨多个性能指标的框架的能力和有效性。
The evolution of social media has led to a trend of posting daily photos on online Social Network Platforms (SNPs). The privacy of online photos is often protected carefully by security mechanisms. However, these mechanisms will lose effectiveness when someone spreads the photos to other platforms. In this article, we propose Go-sharing, a blockchain-based privacy-preserving framework that provides powerful dissemination control for cross-SNP photo sharing. In contrast to security mechanisms running separately in centralized servers that do not trust each other, our framework achieves consistent consensus on photo dissemination control through carefully designed smart contract-based protocols. We use these protocols to create platform-free dissemination trees for every image, providing users with complete sharing control and privacy protection. Considering the possible privacy conflicts between owners and subsequent re-posters in cross-SNP sharing, we design a dynamic privacy policy generation algorithm that maximizes the flexibility of re-posters without violating formers’ privacy. Moreover, Go-sharing also provides robust photo ownership identification mechanisms to avoid illegal reprinting. It introduces a random noise black box in a two-stage separable deep learning process to improve robustness against unpredictable manipulations. Through extensive real-world simulations, the results demonstrate the capability and effectiveness of the framework across a number of performance metrics.