SMarT: A SMT Based Privacy Preserving Smart Meter Streaming Methodology

Abstract

Smart metering is a mechanism through which fine-grained power consumption profiles of the consumers are collected periodically in a Smart grid. However, a growing concern in this regard is that the leakage of consumers’ consumption data may reveal their daily life patterns as the state-of-the-art metering strategies lack adequate security and privacy measures. Since Smart grid communication infrastructure supports low bandwidth, it prohibits the usage of computation-intensive cryptographic solutions. Among different privacy-preserving smart meter streaming methods, data manipulation techniques can easily be implemented in smart meters and do not require installing any storage devices or alternative energy sources. While these proposals are attractive to the privacy-aware smart meter design community, rigorous security evaluations of such schemes highlight their infeasibility by determining individual consumption patterns efficiently, thus compromising their privacy guarantees. Keeping in mind the inadequacies of these schemes, we propose a load signature modification technique, namely Obfuscate-Load-Signature that obscures the input power profile utilizing an information-theoretic metric to bound the inherent private information present in the metering stream. Along with providing the coveted privacy guarantees, the privacy preserved output time series profile generated due to our methodology also ensures excellent system utility by providing no aggregation and billing errors over constant tariff. In summary, we highlight how the aggregated metering information can be transformed to obscure individual consumption patterns without affecting the intended semantics of Smart grid operations. Finally, we present a rigorous experimental validation of our proposed methodology using a real-life dataset and suitable Hardware-In-the-Loop testbed.