Sensitivity inspector: Detecting privacy in smart energy applications

The problem of privacy disclosure hinders large number of ubiquitous applications to collect, disseminate and analyze personal data from providing useful and important services. It is understood that sharing private data has high potential for facilitating innumerable benefits as well as inviting intended or unintended malicious activities leading to severe privacy breach. Such privacy breach attacks mostly capture sensitive or broadly the anomalous events. Fine grained, high resolution smart meter energy consumption data contains sensitive house hold activity signature. In this paper, we propose a tool called `Sensitivity Inspector´ that detects sensitivity in smart meter data and inculcates privacy awareness among smart meter users, presuming private events are related to anomalous or unusual activities. Specifically, we analyze the sensitive content of smart meter data through robust unsupervised statistical method considering user activity as a piece-wise, stationary, stochastic process with associated uncertainty. We show the efficacy of our scheme under relevant statistical and information theoretic measures. We implement our algorithm and compare sensitivity detection capability with related supervised learning based approach and relevant privacy breaching attack like Non-Intrusive Load Monitoring (NILM).