Smart Meters (SMs) are able to share the power consumption of users with utility providers almost in real-time. These fine-grained signals carry sensitive information about users, which has raised serious concerns from the privacy viewpoint. In this paper, we focus on real-time privacy threats, i.e., potential attackers that try to infer sensitive information from SMs data in an online fashion. We adopt an information-theoretic privacy measure and show that it effectively limits the performance of any attacker. Then, we propose a general formulation to design a privatization mechanism that can provide a target level of privacy by adding a minimal amount of distortion to the SMs measurements. On the other hand, to cope with different applications, a flexible distortion measure is considered. This formulation leads to a general loss function, which is optimized using a deep learning adversarial framework, where two neural networks -- referred to as the releaser and the adversary -- are trained with opposite goals. An exhaustive empirical study is then performed to validate the performance of the proposed approach and compare it with state-of-the-art methods for the occupancy detection privacy problem. Finally, we also investigate the impact of data mismatch between the releaser and the attacker.
翻译:智能计量器(SMs)能够与几乎实时的公用事业供应商分享用户的能量消耗。 这些精细的信号含有用户的敏感信息,这引起了隐私观点的严重关切。 在本文中,我们侧重于实时隐私威胁,即试图以在线方式从SMS数据中推断敏感信息的潜在攻击者。 我们采取了信息理论隐私措施, 并表明它有效限制了任何攻击者的表现。 然后, 我们提出一个一般性的提法, 设计一个私有化机制, 通过在 SM 测量中增加最低限度的扭曲, 提供目标隐私水平。 另一方面, 为了应对不同的应用, 考虑一种灵活的扭曲措施。 这种提法导致一种一般损失功能, 使用一个深层次的对抗性框架优化了这种功能, 即两个神经网络 -- -- 被称为释放者和对手 -- -- 受到相反目标的培训。 然后进行一项详尽的实证研究, 以验证拟议方法的性能, 并将它与占用隐私问题的最新方法进行比较。 最后, 我们还调查数据释放者和攻击者之间不匹配的影响。