A Privacy-protection Scheme for Smart Water Grid Based on Blockchain and Machine Learning

Abstract

In Smart Water Grid (SWG), smart water meters (SWMs) are installed in customers ' homes in order to provide near-real-time water consumption data. However, near-real-time data collected by SWMs can reveal the user's privacy. In this paper, we propose a user privacy protection scheme that relies on Blockchain technology and a Machine Learning algorithm called k-means++. K-means++ is used to group users into clusters and, each cluster has a private Blockchain to record its members' data. We use pseudonyms to mask users' identities and the Bloom filter is used for quick authentication. The proposed scheme is validated using simulations in python.