Reporting granular energy usage data from smart meters to power grid enables effective power distribution by smart grid. Demand Response (DR) mechanism incentivize users towards efficient use of energy. However, consumer’s 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.
Khadija Hafeez, Mubashir Husain Rehmani, Donna O'Shea, 'DPNCT: A Differential Private Noise Cancellation Scheme for Load Monitoring and Billing for Smart Meters' to appear in IEEE International Conference on Communications (ICC) 2021 - Workshop on Communication, Computing, and Networking in Cyber-Physical Systems (IEEE CCN-CPS 2021), Montreal, Canada, June 2021.