Internet of things (IoT) devices are becoming increasingly popular thanks to many new services and applications they offer. However, in addition to their many benefits, they raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. In this work, we study the privacy-utility trade-off (PUT) in time-series data sharing. Existing approaches to PUT mainly focus on a single data point; however, temporal correlations in time-series data introduce new challenges. Methods that preserve the privacy for the current time may leak significant amount of information at the trace level as the adversary can exploit temporal correlations in a trace. We consider sharing the distorted version of a user's true data sequence with an untrusted third party. We measure the privacy leakage by the mutual information between the user's true data sequence and shared version. We consider both the instantaneous and average distortion between the two sequences, under a given distortion measure, as the utility loss metric. To tackle the history-dependent mutual information minimization, we reformulate the problem as a Markov decision process (MDP), and solve it using asynchronous actor-critic deep reinforcement learning (RL). We evaluate the performance of the proposed solution in location trace privacy on both synthetic and GeoLife GPS trajectory datasets. For the latter, we show the validity of our solution by testing the privacy of the released location trajectory against an adversary network.

中文翻译：

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通过深度强化学习实现隐私意识时间序列数据共享


物联网 (IoT) 设备因其提供的许多新服务和应用程序而变得越来越受欢迎。然而，除了它们的许多好处之外，它们还引起了隐私问题，因为它们与不受信任的第三方共享细粒度的时间序列用户数据。在这项工作中，我们研究了时间序列数据共享中的隐私与效用权衡（PUT）。现有的 PUT 方法主要关注单个数据点；然而，时间序列数据中的时间相关性带来了新的挑战。保护当前时间隐私的方法可能会在跟踪级别泄露大量信息，因为攻击者可以利用跟踪中的时间相关性。我们考虑与不受信任的第三方共享用户真实数据序列的扭曲版本。我们通过用户真实数据序列和共享版本之间的互信息来衡量隐私泄露。在给定的失真测量下，我们将两个序列之间的瞬时失真和平均失真视为效用损失度量。为了解决历史相关的互信息最小化问题，我们将问题重新表述为马尔可夫决策过程（MDP），并使用异步演员评论家深度强化学习（RL）来解决它。我们在合成和 GeoLife GPS 轨迹数据集上评估了所提出的解决方案在位置跟踪隐私方面的性能。对于后者，我们通过针对对手网络测试发布的位置轨迹的隐私性来展示我们解决方案的有效性。