DPNCT: A Differential Private Noise Cancellation Scheme for Load Monitoring and Billing for Smart Meters

TL;DR

This paper introduces DPNCT, a scheme that enhances privacy in smart meter data by applying differential privacy with noise cancellation, balancing privacy preservation with accurate load monitoring and billing.

Contribution

The paper proposes a novel noise cancellation mechanism for differentially private data, improving utility in load monitoring and billing compared to existing methods.

Findings

DPNCT outperforms existing schemes in privacy preservation.

The noise cancellation improves billing accuracy.

The scheme maintains privacy while enabling effective load monitoring.

Abstract

Highly accurate profiles of consumers daily energy usage are reported to power grid via smart meters which enables smart grid to effectively regulate power demand and supply. However, consumers energy consumption pattern can reveal personal and sensitive information regarding their lifestyle. Therefore, to ensure users privacy, differentially distributed noise is added to the original data. This technique comes with a trade off between privacy of the consumer versus utility of the data in terms of providing services like billing, Demand Response schemes, and Load Monitoring. In this paper, we propose a technique - Differential Privacy with Noise Cancellation Technique (DPNCT) - to maximize utility in aggregated load monitoring and fair billing while preserving users privacy by using noise cancellation mechanism on differentially private data. We introduce noise to the sensitive data stream before it leaves smart meters in order to guarantee privacy at individual level. Further, we evaluate the effects of different periodic noise cancelling schemes on privacy and utility i.e., billing and load monitoring. Our proposed scheme outperforms the existing scheme in terms of preserving the privacy while accurately calculating the bill.