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Aggregating fine-granular data measurements from smart meters presents an opportunity for utility companies to learn about consumers' power consumption patterns. Several research studies have shown that power consumption patterns can reveal a range of information about consumers, such as how many people are in the home, the types of appliances they use, their eating and sleeping routines, and even the TV programs they watch. As we move toward liberalized energy markets, many different parties are interested in gaining access to such data, which has enormous economical, societal, and environmental benefits. However, the main concern is that many such beneficial uses of smart meter big data would be severely curtailed if the data were excessively protected due to individuals' privacy. In this paper, we propose a game theoretic mechanism that balances between beneficial uses of data and individuals' privacy in deregulated smart grids. Our mechanism solves the problem of access control by fairly compensating consumers for their participation in the data market based on the concept of differential privacy. The results of our experiments show the importance of taking consumers' attitudes toward privacy as a crucial element in designing balanced markets for fair data sharing. Furthermore, the experiments provide a principled way to choose reasonable values for privacy levels that are more relevant to real-world scenarios.

Smart Meters Big Data: Game Theoretic Model for Fair Data Sharing in Deregulated Smart Grids