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Personalized Location Privacy in Mobile Networks: A Social Group Utility Approach

机译:移动网络中的个性化位置隐私:社会群体实用方法

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With increasing popularity of location-based services (LBSs), there have been growing concerns for location privacy. To protect location privacy in a LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this study, we leverage the social tie structure among mobile users to motivate them to participate in pseudonym change. Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to change pseudonyms as a socially-aware pseudonym change game (PCG). The PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SGUM-based PCG, we show that there exists a socially-aware Nash equilibrium (SNE), and quantify the system efficiency of the SNE with respect to the optimal social welfare. Then we develop a greedy algorithm that myopically determines users' strategies, based on the social group utility derived from only the users whose strategies have already been determined. It turns out that this algorithm can efficiently find a Pareto-optimal SNE with social welfare higher than that for the socially-oblivious PCG, pointing out the impact of exploiting social tie structure. We further show that the Pareto-optimal SNE can be achieved in a distributed manner.
机译:随着基于位置的服务(LBSS)的普及,越来越多地对位置隐私。为了保护LBS中的位置隐私,物理邻近度的移动用户可以协同起作用,以共同改变它们的假名,以便隐藏其位置迹线中的空间时间相关性。在这项研究中,我们利用移动用户之间的社交领带结构,激励他们参与假名变化。绘制社交群体实用程序最大化(SGUM)框架,我们施放了用户的决策,是如何将假名改变为社会感知的假名更改游戏(PCG)。 PCG进一步假设允许用户为个性化位置隐私设定其特定匿名的匿名模型。对于基于SGUM的PCG,我们表明存在社会感知的纳什均衡(SNE),并量化SNE关于最佳社会福利的系统效率。然后,我们开发了一种贪婪的算法,即神秘地基于从已经确定的策略已经确定的用户的社交组实用程序来确定用户的策略。事实证明,该算法可以有效地找到与社会福利高于社会福利的帕累托最优的SNE,这指出利用社会领带结构的影响。我们进一步表明,帕累托最优的SNE可以以分布式方式实现。

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