计算机科学
方案(数学)
信息共享
设计隐私
互联网隐私
信息敏感性
隐私政策
信息流
背景(考古学)
个人可识别信息
隐私软件
信息隐私
计算机安全
万维网
古生物学
哲学
数学分析
生物
语言学
数学
作者
Yuzi Yi,Nafei Zhu,Jingsha He,Anca Delia Jurcut,Xuelei Ma,Yehong Luo
标识
DOI:10.1016/j.comcom.2023.01.010
摘要
Privacy leakage resulting from information sharing in online social networks (OSNs) is a serious concern for individuals. One of the culprits behind the problem is that existing privacy policies developed for OSNs are not fine-grained or flexible enough, resulting in privacy settings that could hardly meet the privacy requirements of individuals. Neither would such privacy settings allow individuals to control where the information could go. In addition, there are hardly any effective mechanisms for measuring potential threats to privacy during information propagation. To alleviate the situation, in this paper, we propose a novel privacy-preserving information sharing scheme for OSNs in which information flow can be controlled according to the privacy requirements of the information owner and the context of the information flow. Specifically, we first formally define the privacy-dependent condition (PDC) for information sharing in OSNs and then design a PDC-based privacy-preserving information sharing scheme (PDC-InfoSharing) to protect the privacy of individuals according to the heterogeneous privacy requirements of individuals as well as the potential threats that they may face. Furthermore, to balance information sharing and privacy protection, the techniques of reinforcement learning is utilized to help individuals reach a trade-off. PDC-InfoSharing would allow the privacy policies for specific information audience to be derived based on PDC to achieve dynamical control of the flow of information. Theoretical analysis proves that the proposed scheme can assist individuals in adopting fine-grained privacy policies and experiment shows that it can adapt to different situations to help individuals achieve the trade-off between information sharing and privacy protection.
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