  Utilities around the world are reported to invest a total of around 30
billion over the next few years for installation of more than 300 million smart
meters, replacing traditional analog meters [1]. By mid-decade, with full
country wide deployment, there will be almost 1.3 billion smart meters in place
[1]. Collection of fine grained energy usage data by these smart meters
provides numerous advantages such as energy savings for customers with use of
demand optimization, a billing system of higher accuracy with dynamic pricing
programs, bidirectional information exchange ability between end-users for
better consumer-operator interaction, and so on. However, all these perks
associated with fine grained energy usage data collection threaten the privacy
of users. With this technology, customers' personal data such as sleeping
cycle, number of occupants, and even type and number of appliances stream into
the hands of the utility companies and can be subject to misuse. This research
paper addresses privacy violation of consumers' energy usage data collected
from smart meters and provides a novel solution for the privacy protection
while allowing benefits of energy data analytics. First, we demonstrate the
successful application of occupancy detection attacks using a deep neural
network method that yields high accuracy results. We then introduce Adversarial
Machine Learning Occupancy Detection Avoidance with Blockchain (AMLODA-B)
framework as a counter-attack by deploying an algorithm based on the Long Short
Term Memory (LSTM) model into the standardized smart metering infrastructure to
prevent leakage of consumers personal information. Our privacy-aware approach
protects consumers' privacy without compromising the correctness of billing and
preserves operational efficiency without use of authoritative intermediaries.
