A Hybrid Privacy Protection Scheme in Cyber-Physical Social Networks
A Hybrid Privacy Protection Scheme in Cyber-Physical Social Networks
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一种信息物理社会网络中的混合隐私保护方案
DOI：
10.1109/tcss.2018.2861775
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发表时间：
2018-08
影响因子：
5
通讯作者：
Youyang Qu;Shui Yu;Longxiang Gao;Wanlei Zhou;Sancheng Peng
中科院分区：
文献类型：
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作者：
Youyang Qu;Shui Yu;Longxiang Gao;Wanlei Zhou;Sancheng Peng
智能移动设备的迅速普及显著促进了网络-物理社交网络的普及，用户在社交网络中主动发布包含敏感信息的数据。攻击者可以很容易地获得这些数据，并发起持续的攻击来侵犯隐私。然而，现有的研究只关注静态对手的位置隐私或身份隐私。这会导致隐私泄露，并可能造成进一步的损害。受此启发，我们提出了一种混合隐私保护方案，该方案同时考虑了位置隐私和身份隐私，以对抗动态对手。我们研究隐私保护问题是在用户以最大化数据效用为目标与高级别隐私保护之间的权衡，而对手拥有相反的目标。首先建立了一个基于博弈的马尔可夫决策过程模型，将用户和对手视为动态多阶段零和博弈中的两个博弈主体。为了获得用户的最佳策略，我们采用了一种改进的状态-动作-奖励-状态-动作强化学习算法。由于基数从$n$减少到2，迭代次数减少，从而加快了收敛过程。在真实数据集上的大量实验证明了该方法的有效性和可行性。
The rapid proliferation of smart mobile devices has significantly enhanced the popularization of the cyber-physical social network, where users actively publish data with sensitive information. Adversaries can easily obtain these data and launch continuous attacks to breach privacy. However, existing works only focus on either location privacy or identity privacy with a static adversary. This results in privacy leakage and possible further damage. Motivated by this, we propose a hybrid privacy-preserving scheme, which considers both location and identity privacy against a dynamic adversary. We study the privacy protection problem as the tradeoff between the users aiming at maximizing data utility with high-level privacy protection while adversaries possessing the opposite goal. We first establish a game-based Markov decision process model, in which the user and the adversary are regarded as two players in a dynamic multistage zero-sum game. To acquire the best strategy for users, we employ a modified state-action-reward-state-action reinforcement learning algorithm. Iteration times decrease because of cardinality reduction from $n$ to 2, which accelerates the convergence process. Our extensive experiments on real-world data sets demonstrate the efficiency and feasibility of the propose method.