Abstract:
The way energy is provided by electricity providers is changed because of
various upgrades in power grid. Advanced Metering Infrastructure (AMI)
is one of the main reasons to modernise the the electricity grid. There are
some privacy concerns associated with this electricity grid. This process can
reveal the private information of consumer’s as it collects fine-grained power
consumption data. This has led to limited consumer acceptance of the smart
grid. Hence, it is important to design some mechanism to prevent disclosure
of consumer electricity usage information. Security researchers have provided
a lot of efforts in various private data aggregation techniques. In this the sis, elliptic curve and Diffie–Hellman based privacy preserving aggregation
scheme is proposed with very less computation overhead. It’s performance is
evaluated and validated by statistical analysis and by testing it on a dataset.
This scheme provides promising solution for fine-grained load monitoring,
secure billing, dynamic tariffs, accountability, fault tolerance, selective un masking of energy readings altogether in a very efficient way comparative
to other schemes. This scheme is applicable on limited-capability smart me ters. So, this work is an important progress toward more reliable, secure and
authentic smart meter communication.
