Smart grids are modern implementations of energy grids, where each consumers uses a smart meter to collect consumption data in short time intervals. this consumption value is reported to and stored in a data center, which the supplier uses for effective maintenance and control of the energy grid. However, such a fine-grid energy profile raises privacy concerns for the consumers. In order to preserve privacy of the consumers, the researchers have proposed masking the data using noise addition. However, this method significantly reduces the potential for deduplication and compression of the consumption data in the data center that collects the data. In this work, we propose an innovative technique to provide privacy for the consumers in a smart grid environment, while allowing the data center to reduce the footprint of the data by performing efficient deduplication and compression on the consumption data. Our results show that using this technique, the data center can achieve a compression rate of 21.99%, allowing the system to scale and provide service to many consumers.
|Submitted - 2022