Privacy for IoT: Involuntary privacy enablement for smart energy systems

Smart meter, the important component of smart energy management systems invites intended or unintended, possibly dangerous privacy breaching activities, like in-house activity detection. With the emergence of Non-Intrusive Load Monitoring (NILM), privacy preservation of smart meter data becomes very important for an individual. Emerging solution provides privacy breach minimization by supervised learning through training that incurs higher capex and opex. IoT systems do not consist of human-in-loop. So, involuntary approach of privacy preservation is to be employed. In this paper, we propose a novel solution for addressing the problem of involuntary privacy breaching risk minimization in smart energy management systems. Our proposed solution `Dynamic Privacy Analyzer' scheme is an attempt towards achieving a unique privacy metric that is derived from fundamental principles like robust statistics and information theory. We analyze the performance of our scheme with large set of publicly available real smart meter datasets and evaluate optimality criteria like utility-privacy trade-off. Efficacy of our proposed scheme is demonstrated by minimizing the capability of privacy intruders like NILM. To the best of our knowledge, for the first time the involuntary privacy-aware scheme tailored for IoT system is proposed. Our proposed scheme is generic enough to suit in other IoT applications.