A Privacy Preserving Approach to Smart Metering

High frequency power consumption readings produced by smart meters introduce a major privacy threat to residential consumers as they reveal details that could be used to infer information about the activities of home occupants. In this paper, we question the need to disclose high frequency readings produced at the home’s level. Instead, we propose equipping smart meters with sufficient processing power enabling them to provide the utility company with a set of well-defined services based on these readings. For demand side management, we propose the collection of high frequency readings at a higher level in the distribution network, such as local step-down transformers, as this readily provides the accumulated demand of all homes within a branch. Furthermore, we study the effect of the proposed approach on consumers’ privacy, using correlation and relative entropy as measures. We also study the effect of load balancing on consumers’ privacy when using the proposed approach. Finally, we assess the detection of different appliances using high frequency readings collected for demand side management purposes.