Smart Meter Privacy: Adversarial Hypothesis Testing Models
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Smart meter privacy and privacy-preserving energy management are studied in the presence of a renewable energy source. The privacy leakage is measured by the probability of error of an adversarial binary hypothesis test, which tries to detect the consumer behavior based on the meter readings. An optimal privacy-preserving energy management policy maximizes the minimal Type II probability of error subject to a constraint on the Type I probability of error under an adversarial Neyman-Pearson test, or maximizes the minimal error probability under an adversarial Bayesian test. The asymptotic exponential decay rates of the maximum minimal Type II probability of error and the maximum minimal error probability are shown to be characterized by a Kullback-Leibler divergence rate and a Chernoff information rate, respectively. Privacy performances of two special energy management policies, the memoryless hypothesis-aware policy and the hypothesis-unaware policy with memory, are compared. It is also shown that the energy supply alphabet can be constrained to the energy demand alphabet without loss of optimality for the evaluations of single-letter asymptotic privacy guarantees.
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