Document Type

Conference Object


Computer Sciences

Publication Details

This paper has been accepted to the International Conference on Communications (ICC) 2021 - Workshop on Communication, Computing, and Networking in Cyber-Physical Systems (IEEE CCN-CPS 2021) which will take place virtually from 14-23rd June, 2021 in Montreal, Canada.


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.