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.

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