ABDP: Accurate Billing on Differentially Private Data Reporting for Smart Grids

While smart grid significantly facilitates energy efficiency by using users’ power consumption data, it poses privacy leakage risk for user personal behaviors. Differential privacy (DP) has emerged as a promising solution to address this issue. However, existing approaches suffer from severe data utility degradation due to the intensive noise introduced by DP. Additionally, some of these methods are vulnerable to security attacks. To bridge this gap, in this paper, we propose ABDP ( a ccurate b illing-enabled d ifferentially p rivate), a mechanism that achieves high-strength DP while ensuring accurate aggregation and billing operations without compromising security. In particular, we propose aggregated and individual noise cancellation algorithms to counteract the negative effects of noise on data utility. Specifically, our ABDP ensures precise aggregation and accurate billing calculations for the power grid and individual users, respectively Furthermore, we present a Blockchain smart contract exploiting the pseudo random function to enforce a fair and secure data reporting process. Theoretical analysis is provided to evaluate the privacy and security guarantees of ABDP. Experimental results on real-world datasets, namely NERL-DATA and REDD, demonstrate that ABDP achieves error-free aggregation and billing calculation, offers arbitrary intensity privacy protection against non-intrusive load monitoring and filtering attacks, and outperforms existing state-of-the-art approaches.