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.

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Author Of this post: <a href="http://arxiv.org/find/cs/1/au:+Hafeez_K/0/1/0/all/0/1">Khadija Hafeez</a>, <a href="http://arxiv.org/find/cs/1/au:+Rehmani_M/0/1/0/all/0/1">Mubashir Husain Rehmani</a>, <a href="http://arxiv.org/find/cs/1/au:+OShea_D/0/1/0/all/0/1">Donna OShea</a>

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