# Learning Defensive Policies against Diverse Inference Attacks for Smart Meter Privacy

arXiv: https://arxiv.org/abs/2609.25484 · HTML: https://mlsanity.com/paper/2609.25484/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [A Survey on Transferability of Adversarial Examples across Deep Neural Networks](https://mlsanity.com/paper/2310.17626/) (2023)
- [Adversarial Examples in Deep Learning for Multivariate Time Series Regression](https://mlsanity.com/paper/2009.11911/) (2020)
- [Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02685/) (2020)
- [Convolutional Sequence to Sequence Non-intrusive Load Monitoring](https://mlsanity.com/paper/1806.02078/) (2018)
- [Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2207.02009/) (2022)
- [NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity](https://mlsanity.com/paper/2506.05880/) (2025)
- [Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning](https://mlsanity.com/paper/2003.04946/) (2020)
- [Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning](https://mlsanity.com/paper/2006.06106/) (2020)
- [Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning](https://mlsanity.com/paper/2405.18888/) (2024)
- [INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression](https://mlsanity.com/paper/2604.11928/) (2026)

## Abstract (arXiv)

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%.