Smart meter (SM) data provides fine-grained visibility into household energy consumption, but also exposes users to privacy risks.
Inference attacks, known as non-intrusive load monitoring (NILM), can perform appliance-level inference from aggregate signals and recover sensitive behavioral patterns.
In practice, attacker models are unknown and heterogeneous, making robust defense challenging.
We formulate SM privacy protection as a black-box inference defense problem, aiming to reduce the recoverability of appliance-level information while generalizing across diverse and unseen attackers.
We propose a proxy-guided hierarchical reinforcement learning framework that learns battery-based load-shaping policies to inject realistic but misleading appliance-level signatures into the aggregate signal, thereby disrupting the structured patterns exploited by NILM.
A self-supervised aggregate-structure privacy probe provides a reconstruction-error-based surrogate reward for disrupting recoverable load structure, while a signature library makes the perturbations appliance-relevant and physically realizable through battery control.
We provide theoretical rationale showing that proxy-guided optimization improves inference robustness under attacker diversity.
Experiments on real-world datasets UK-DALE and REDD demonstrate strong cross-model and cross-appliance generalization.
Across six unseen NILM attackers, covering four appliances on UK-DALE and five on REDD, our proposed defense increases average appliance-level RMSE by 107% and 166%, respectively, while reducing F1 score by 79% and 80%.