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Game-Theoretic Model of Incentivizing Privacy-Aware Users to Consent to Location Tracking

机译:激励隐私意识用户同意位置跟踪的博弈模型

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摘要

Nowadays, mobile users have a vast number of applications and services at their disposal. Each of these might impose some privacy threats on users' "Personally Identifiable Information" (PII). Location privacy is a crucial part of PII, and as such, privacy-aware users wish to maximize it. This privacy can be, for instance, threatened by a company, which collects users' traces and shares them with third parties. To maximize their location privacy, users can decide to get offline so that the company cannot localize their devices. The longer a user stays connected to a network, the more services he might receive, but his location privacy decreases. In this paper, we analyze the trade-off between location privacy, the level of services that a user experiences, and the profit of the company. To this end, we formulate a Stackelberg Bayesian game between the User (follower) and the Company (leader). We present theoretical results characterizing the equilibria of the game. To the best of our knowledge, our work is the first to model the economically rational decision-making of the service provider (i.e., the Company) in conjunction with the rational decision making of users who wish to protect their location privacy. To evaluate the performance of our approach, we have used real-data from a testbed, and we have also shown that the game-theoretic strategy of the Company outperforms non-strategic methods. Finally, we have considered different User privacy types, and have determined the service level that incentivizes the User to stay connected as long as possible.
机译:如今,移动用户可以使用大量的应用程序和服务。这些中的每一个都可能对用户的“个人身份信息”(PII)构成一些隐私威胁。位置隐私是PII的关键部分,因此,具有隐私意识的用户希望将其最大化。例如,此隐私可能受到公司的威胁,该公司收集用户的踪迹并将其与第三方共享。为了最大化其位置隐私,用户可以决定下线,以便公司无法本地化其设备。用户停留在网络上的时间越长,他可能会收到更多的服务,但是他的位置隐私性下降。在本文中,我们分析了位置隐私,用户体验的服务水平以及公司利润之间的权衡。为此,我们制定了用户(追随者)和公司(领导者)之间的Stackelberg贝叶斯游戏。我们提供表征游戏平衡性的理论结果。据我们所知,我们的工作是第一个将服务提供商(即公司)的经济合理决策与希望保护其位置隐私的用户的合理决策结合起来的模型。为了评估我们的方法的性能,我们使用了来自测试平台的真实数据,并且我们还表明,公司的博弈论策略优于非策略性方法。最后,我们考虑了不同的用户隐私类型,并确定了激励用户尽可能长时间保持联系的服务级别。

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