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      Privacy-Aware Smart Metering: Progress and Challenges

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          Abstract

          The next-generation energy network, the so-called smart grid (SG), promises a tremendous increase in efficiency, safety and flexibility of managing the electricity grid as compared to the legacy energy network. This is needed today more than ever as the global energy consumption is growing at an unprecedented rate, and renewable energy sources have to be seamlessly integrated into the grid to assure a sustainable human development. Smart meters (SMs) are among the crucial enablers of the SG concept; they supply accurate near real-time information about users' household energy consumption to a utility provider, which is essential for time-of-use pricing, rapid fault detection, energy theft prevention, and also provide users more flexibility and control on their consumption. However, highly accurate and granular SM data also poses a threat to consumer privacy as non-intrusive load monitoring techniques enable a malicious attacker to infer many details of a user's private life. This article focuses on privacy-enhancing energy management techniques that provide accurate energy consumption information to the grid operator, without sacrificing consumer privacy. In particular, we focus on techniques that shape and modify the actual user energy consumption by means of physical resources, such as rechargeable batteries, renewable energy sources or demand shaping. A rigorous mathematical analysis of privacy is presented under various physical constraints on the available physical resources. Finally, open questions and challenges that need to be addressed to pave the way to the effective protection of users' privacy in future SGs are presented.

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          Most cited references18

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          Modeling of Load Demand Due to EV Battery Charging in Distribution Systems

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            Simulation-Based Computation of Information Rates for Channels With Memory

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              Private memoirs of a smart meter

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                Author and article information

                Journal
                04 February 2018
                Article
                1802.01166
                81fe1112-bc79-4c7c-af79-511e379e8693

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.IT math.IT

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