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This paper proposes a proxy-guided hierarchical reinforcement learning framework to defend against diverse inference attacks on smart meter data by learning battery-based load-shaping policies that disrupt non-intrusive load monitoring patterns.
This paper formalizes behavioral privacy leakage in multi-round agentic negotiation and proposes an adaptive stochastic policy that provides differential privacy guarantees while maintaining high negotiation utility.