论文标题
积极的隐私 - 实用性权衡反对时间序列数据共享的推论
Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing
论文作者
论文摘要
由于他们提供的服务，物联网（物联网）设备（例如智能电表，智能扬声器和活动监视器）已变得非常受欢迎。但是，除了许多好处外，它们还引起了隐私问题，因为他们与不受信任的第三方共享精细的时间序列用户数据。在这项工作中，我们考虑了一个用户释放包含个人信息的数据，以返回诚实但有趣的服务提供商（SP）的服务。我们将用户的个人信息建模为两个相关的随机变量（R.V.），其中一个称为“秘密变量”，将保持私密，而另一个称为有用变量，将用于实用程序。我们考虑主动的顺序数据发布，在此时间步骤中，用户从一组有限的发布机制中选择的每个步骤都会揭示有关用户个人信息的一些信息，即R.V.的真实值，尽管具有不同的统计信息。用户以在线方式管理数据发布，以便尽快揭示有关潜在有用变量的最大信息，而对敏感变量的信心保持在预定义的水平以下。对于隐私度量，我们既考虑正确检测秘密的真实价值的可能性，又要考虑秘密和已发布数据之间的互信息（MI）。我们将这两个问题提出为部分可观察到的马尔可夫决策过程（POMDP），并通过优势参与者 - 批评（A2C）深度强化学习（DRL）来解决它们。我们评估了合成数据和吸烟活动数据集的拟议策略的隐私 - 实用性权衡（PUT），并通过测试由长期短期记忆（LSTM）神经网络模型的SP的活动检测准确性来显示其有效性。
Internet of things (IoT) devices, such as smart meters, smart speakers and activity monitors, have become highly popular thanks to the services 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 consider a user releasing her data containing personal information in return of a service from an honest-but-curious service provider (SP). We model user's personal information as two correlated random variables (r.v.'s), one of them, called the secret variable, is to be kept private, while the other, called the useful variable, is to be disclosed for utility. We consider active sequential data release, where at each time step the user chooses from among a finite set of release mechanisms, each revealing some information about the user's personal information, i.e., the true values of the r.v.'s, albeit with different statistics. The user manages data release in an online fashion such that the maximum amount of information is revealed about the latent useful variable as quickly as possible, while the confidence for the sensitive variable is kept below a predefined level. For privacy measure, we consider both the probability of correctly detecting the true value of the secret and the mutual information (MI) between the secret and the released data. We formulate both problems as partially observable Markov decision processes (POMDPs), and numerically solve them by advantage actor-critic (A2C) deep reinforcement learning (DRL). We evaluate the privacy-utility trade-off (PUT) of the proposed policies on both the synthetic data and smoking activity dataset, and show their validity by testing the activity detection accuracy of the SP modeled by a long short-term memory (LSTM) neural network.
