A distributed approach to privacy-preservation and integrity assurance of smart metering data

Smart grid service providers collect metering data at frequent intervals for providing grid and billing functionalities. Studies have shown that access to the granular metering data can lead to breaches in customers' privacy. Several aggregation-based privacy-preserving frameworks for smart metering data have been proposed in the literature. However, these frameworks have either a high computational overhead on resource-constrained smart meters and/or are prone to single points of compromise due to centralized designs. Distributed frameworks with outsourced aggregation can provide the desired functionalities while keeping the framework lightweight for the smart meters. However, these distributed frameworks assume an honest-but-curious adversary, which is not a realistic assumption for outsourced aggregation. This work-in-progress paper proposes a distributed aggregation-based privacy-preserving metering data collection framework under a malicious adversarial model (dishonest majority of aggregators). This framework is capable of verifying the integrity of the spatio-temporal metering data while ensuring customers' privacy. The performance analysis of the proposed framework demonstrates that it outperforms a closely related existing framework with similar customer privacy and integrity verification goals. Our results on the computational overhead on smart meters, end-to-end delay, scalability, and resilience against threats to privacy and integrity are presented in this paper.