Smart Meter Privacy With Renewable Energy and an Energy Storage Device

Abstract

A smart meter (SM) measures a consumer's electricity consumption and reports it automatically to a utility provider (UP) in almost real time. Despite many advantages of SMs, their use also leads to serious concerns about consumer privacy. In this paper, SM privacy is studied by considering the presence of a renewable energy source (RES) and a rechargeable battery (RB), which can be used to partially hide the consumer's energy consumption behavior. Privacy is measured by the information leakage rate, which denotes the average mutual information between the user's real energy consumption and the energy requested from the grid, which the SM reads and reports to the UP. The impact of the knowledge of the amount of energy generated by the RES at the UP is also considered. The minimum information leakage rate is characterized as a computable information theoretic single-letter expression in the two extreme cases, that is, when the battery capacity is infinite or zero. Numerical results are presented for the finite battery capacity case to illustrate the potential privacy gains from the existence of an RB. It is shown that, while the information leakage rate decreases with increasing availability of an RES, larger storage capacity is needed to fully exploit the available energy to improve the privacy.

Privacy-Utility Management of Hypothesis Tests

“…Therefore, we have (φ k s,λ ) l ∈ Φ kl s,λ since the policy (φ k s,λ ) l also satisfies the constraint in (6). It follows that lim inf…”

“…Privacy-utility trade-offs have been studied in different contexts [1]- [9]. The privacy leakage can be modeled as a statistical inference and measured by the mutual information [1]- [3], [6], [7], divergence [4], differential privacy [9], or variance [8]. Depending on the application, the utility measure can be the expectation of cost [5], [6], [8], divergence [4], [7], or data rate [1], [3].…”

“…The privacy leakage can be modeled as a statistical inference and measured by the mutual information [1]- [3], [6], [7], divergence [4], differential privacy [9], or variance [8]. Depending on the application, the utility measure can be the expectation of cost [5], [6], [8], divergence [4], [7], or data rate [1], [3]. With the privacy and utility measures, the trade-off problem can be formulated as a worst case analysis [2], [4], [6]- [8] or zero-sum game [5].…”

“…Depending on the application, the utility measure can be the expectation of cost [5], [6], [8], divergence [4], [7], or data rate [1], [3]. With the privacy and utility measures, the trade-off problem can be formulated as a worst case analysis [2], [4], [6]- [8] or zero-sum game [5].…”

Privacy-Aware Location Sharing with Deep Reinforcement Learning

“…Time-series data privacy and its applications to various domains have been extensively studied [5]- [25]. A large body of research has focused on protecting the privacy of a single data point, e.g., the current sensitive measurement [12]- [17]. However, the temporal relations in time-series data requires going beyond single data point privacy.…”