*WINNER* Use of Adversarial Machine Learning for Avoiding Occupancy Detection from Smart Meter

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 the smart meters raises additional security and privacy concerns for users and companies. Occupancy detection is one such example which causes privacy violation of smart meter users. Detecting the occupancy of a home is straightforward with the time of use information as there is a strong correlation between occupancy and electricity usage. However, most of the existing privacy preserving solutions use crypto-graphical techniques that are computationally expensive for resource restrained smart meters. 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 the Long Short Term Memory (LSTM) method and demonstrate improved results. In addition, we introduce an Adversarial Machine Occupancy Detection Avoidance (AMODA) framework as a counter attack in order to prevent abuse of energy consumption. Essentially, the proposed privacy-preserving framework is designed to mask the real-time or near real-time electricity usage information using calculated optimum noise without compromising the users’ billing systems functionality. Our results show that the proposed privacy-aware billing technique upholds users’ privacy strongly.