Privacy-Aware Profiling and Statistical Data Extraction for Smart Sustainable Energy Systems

The growing population and global warming have been calling for more effective energy usage, which have stimulated the emergence of smart sustainable energy technology. The distinct feature of this newly emerging technology is the incorporation of advanced information and communication technologies (ICT), which collects more detailed information on how energy is generated, distributed, and consumed. Various smart metering technologies have also been proposed to support the optimization on sustainable energy usage. Despite the obvious benefits of these technologies, people may still hesitate to adopt them because of possible privacy breach. On the other hand, we observe that the major target information for making the sustainable energy system smart is the aggregated statistics of energy usage, not the full detailed usage profiles which would compromise customers' privacy. Thus, how to design schemes to collect aggregated statistics while preserving customers' privacy becomes an important research problem. In this paper, we propose two schemes to deal with this problem. The first one can support dynamic profiling, which can extract aggregated statistical information without compromising individual privacy. The second one aims to extract correlation information among various factors for the smart system design and can also be used as an underlying tool for baseline inference and association rule mining.