Avoiding Occupancy Detection from Smart Meter using Adversarial Machine Learning

TL;DR

This paper demonstrates how occupancy detection attacks can be performed using LSTM models on smart meter data and proposes an adversarial machine learning framework to effectively mask usage data, enhancing user privacy.

Contribution

The paper introduces a novel adversarial machine learning framework, AMLODA, to prevent occupancy detection attacks on smart meters, improving privacy preservation techniques.

Findings

LSTM-based occupancy detection is highly effective.

AMLODA significantly reduces detection accuracy.

Privacy-preserving billing maintains billing accuracy.

Abstract

More and more conventional electromechanical meters are being replaced with smart meters because of their substantial benefits such as providing faster bi-directional communication between utility services and end users, enabling direct load control for demand response, energy saving, and so on. However, the fine-grained usage data provided by smart meter brings additional vulnerabilities from users to companies. Occupancy detection is one such example which causes privacy violation of smart meter users. Detecting the occupancy of a home is straightforward with time of use information as there is a strong correlation between occupancy and electricity usage. In this work, our major contributions are twofold. First, we validate the viability of an occupancy detection attack based on a machine learning technique called Long Short Term Memory (LSTM) method and demonstrate improved results. In addition, we introduce an Adversarial Machine Learning Occupancy Detection Avoidance (AMLODA) framework as a counter attack in order to prevent abuse of energy consumption. Essentially, the proposed privacy-preserving framework is designed to mask real-time or near real-time electricity usage information using calculated optimum noise without compromising users' billing systems functionality. Our results show that the proposed privacy-aware billing technique upholds users' privacy strongly.