Innovative Privacy-preserving Aggregation Techniques for Smart Meters

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.