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Highly accurate profiles of consumers daily energy usage are reported topower grid via smart meters which enables smart grid to effectively regulatepower demand and supply. However, consumers energy consumption pattern canreveal personal and sensitive information regarding their lifestyle. Therefore,to ensure users privacy, differentially distributed noise is added to theoriginal data. This technique comes with a trade off between privacy of theconsumer versus utility of the data in terms of providing services likebilling, Demand Response schemes, and Load Monitoring. In this paper, wepropose a technique - Differential Privacy with Noise Cancellation Technique(DPNCT) - to maximize utility in aggregated load monitoring and fair billingwhile preserving users privacy by using noise cancellation mechanism ondifferentially private data. We introduce noise to the sensitive data streambefore it leaves smart meters in order to guarantee privacy at individuallevel. Further, we evaluate the effects of different periodic noise cancellingschemes on privacy and utility i.e., billing and load monitoring. Our proposedscheme outperforms the existing scheme in terms of preserving the privacy whileaccurately calculating the bill.

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