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Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning

Ruichang Zhang, Youcheng Sun, Mustafa A. Mustafa

TL;DR

This work tackles privacy leaks in smart-meter data by proposing PLS-DQN, a proactive load-shaping framework that employs a rechargeable battery to generate artificial load patterns and mask real consumption. The method casts the problem as a time-discrete finite-horizon MDP with state $S=(D_t,B_t)$, action $a_t$, and a reward that balances privacy, cost, and battery feasibility, learned via a DQN with a target network. A novel reward-shaping strategy using a sliding-window threshold and a battery-consistency mechanism guides the agent toward privacy-centric actions while controlling energy costs. Evaluations against a Seq2Point NILM adversary on UK-DALE data show that PLS-DQN substantially reduces leakages and misleads NILM more effectively than prior work, while maintaining acceptable costs and ensuring battery readiness.

Abstract

Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent scholarly efforts have focused on developing battery-aided load-shaping techniques to protect user privacy while balancing costs. This paper proposes a novel deep reinforcement learning-based load-shaping algorithm (PLS-DQN) designed to protect user privacy by proactively creating artificial load signatures that mislead potential attackers. We evaluate our proposed algorithm against a non-intrusive load monitoring (NILM) adversary. The results demonstrate that our approach not only effectively conceals real energy usage patterns but also outperforms state-of-the-art methods in enhancing user privacy while maintaining cost efficiency.

Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning

TL;DR

This work tackles privacy leaks in smart-meter data by proposing PLS-DQN, a proactive load-shaping framework that employs a rechargeable battery to generate artificial load patterns and mask real consumption. The method casts the problem as a time-discrete finite-horizon MDP with state , action , and a reward that balances privacy, cost, and battery feasibility, learned via a DQN with a target network. A novel reward-shaping strategy using a sliding-window threshold and a battery-consistency mechanism guides the agent toward privacy-centric actions while controlling energy costs. Evaluations against a Seq2Point NILM adversary on UK-DALE data show that PLS-DQN substantially reduces leakages and misleads NILM more effectively than prior work, while maintaining acceptable costs and ensuring battery readiness.

Abstract

Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent scholarly efforts have focused on developing battery-aided load-shaping techniques to protect user privacy while balancing costs. This paper proposes a novel deep reinforcement learning-based load-shaping algorithm (PLS-DQN) designed to protect user privacy by proactively creating artificial load signatures that mislead potential attackers. We evaluate our proposed algorithm against a non-intrusive load monitoring (NILM) adversary. The results demonstrate that our approach not only effectively conceals real energy usage patterns but also outperforms state-of-the-art methods in enhancing user privacy while maintaining cost efficiency.
Paper Structure (16 sections, 10 equations, 5 figures, 1 table, 2 algorithms)

This paper contains 16 sections, 10 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. System Model
  4. Markov Decision Process Formulation
  5. State space $S$
  6. Action space $A$
  7. Transition Probability $P(s_{t+1} \mid s_t, a_t )$
  8. Reward $R$
  9. Discount factor $\gamma$
  10. Methdology
  11. DQN-based Load-Shaping Strategy
  12. NILM-based Adversary Model
  13. Results and discussion
  14. Dataset and Parameter Assignments
  15. Performance of PLS-DQN
  16. ...and 1 more sections

Figures (5)

  • Figure 1: System Model.
  • Figure 2: User load demand and masked load after different load-shaping strategies. (a) The result of DDQL-MI algorithm shateri2020privacy. DDQL-MI aims to minimize mutual information between the original load and masked load. (b) The result of our proposed PLS-DQN algorithm. PLS-DQN aims to introduce artificial load signatures while hiding original load signatures.
  • Figure 3: The impact of the privacy-cost trade-off factor $\lambda$ on results of PLS-DQN algorithm.
  • Figure 4: Load disaggregation of kettle.
  • Figure 5: Load disaggregation of toaster.