Smart Meters (SMs) are a fundamental component of smart grids, but they carry sensitive information about users such as occupancy status of houses and therefore, they have raised serious concerns about leakage of consumers' private information. In particular, we focus on real-time privacy threats, i.e., potential attackers that try to infer sensitive data from SMs reported data in an online fashion. We adopt an information-theoretic privacy measure and show that it effectively limits the performance of any real-time attacker. Using this privacy measure, 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 performances of the proposed approach for the occupancy detection privacy problem, assuming the attacker disposes of either limited or full access to the training dataset.
翻译:智能计量器(SMs)是智能网格的基本组成部分,但它们含有关于用户的敏感信息,例如房屋占用状况,因此,它们引起了消费者私人信息泄漏的严重关切;特别是,我们侧重于实时隐私威胁,即试图从SMs在线报告的数据中推断敏感数据的潜在的攻击者;我们采取了信息理论隐私措施,并表明它有效地限制了任何实时攻击者的表现;使用这一隐私措施,我们提出一个一般性的提法,以设计一个私有化机制,通过给SMs测量量增加最低限度的扭曲,可以提供隐私目标水平;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,采用深层学习的对立框架加以优化,即两个神经网络称为释放器,对手为美元,对手为美元,用相反的目标进行培训;然后进行一项详尽的经验研究,以验证拟议的占用隐私问题探测方法的性能,假设攻击者对培训进行有限或完全访问。