Smart meters provide fine-grained power usage profiles of consumers to various utility providers, thus facilitating multiple grid functionalities such as load monitoring, Real-Time Pricing (RTP), demand response, and so on. However, information leakage from such usage profiles reveals consumers’ private day-to-day life patterns and their home presence/absence, 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. For this purpose, Differential Privacy (DP) is widely adopted in the literature due to its solid mathematical foundation. However, the effect of such manipulations on the RTP control is worth exploring since pricing signals operate in a closed-loop between consumers and utilities. This brings up the privacy-utility tradeoff problem between the user’s achieved privacy and the performance of the pricing loop of Smart grids, an area where the characterization between privacy and pricing control performance is not yet established. We analytically highlight such privacy-utility tradeoff in the closed-loop RTP systems in terms of achieved privacy and the overall generation scheduling errors. We utilize the notion of \(w\) -event privacy and present a RTP aware DP scheme that promises strong user privacy and guarantees pricing signal stabilization irrespective of the privacy level of the DP mechanism. Finally, we show the efficiency and robustness of our scheme by performing extensive experimental validation on MATLAB and, subsequently, on an in-house smart meter test bed.

Smart metering is a mechanism through which fine-grained electricity usage data of consumers is 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. Many proposed solutions have demonstrated how the aggregated metering information can be transformed to obscure individual consumption patterns without affecting the intended semantics of smart grid operations. In this paper, we expose a complete break of such an existing privacy preserving metering scheme [10] by determining individual consumption patterns efficiently, thus compromising its privacy guarantees. The underlying methodol-ogy of this scheme allows us to - i) retrieve the lower bounds of the privacy parameters and ii) establish a relationship between the privacy preserved output readings and the initial input readings. Subsequently, we present a rigorous experimental validation of our proposed attacking methodology using real-life dataset to highlight its efficacy. In summary, the present paper queries: Is the Whole lesser than its Parts? for such privacy aware metering algorithms which attempt to reduce the information leakage of aggregated consumption patterns of the individuals.