Privacy-Utility Tradeoff for Applications Using Energy Disaggregation of Smart-Meter Data

Privacy-preserving data mining technologies have been studied extensively, and as a general approach, du Pin Calmon and Fawaz have proposed a data distortion mechanism based on a statistical inference attack framework. This theory has been extended by Erdogdu et al. to time-series data and been applied to energy disaggregation of smart-meter data. However, their theory assumes both smart-meter data and sensitive appliance state information are available when applying the privacy-preserving mechanism, which is impractical in typical smart-meter systems where only the total power usage is available. In this paper, we extend their approach to enable the application of a privacy-utility tradeoff mechanism to such practical applications. Firstly, we define a system model which captures both the architecture of the smart-meter system and the practical constraints that the power usage of each appliance cannot be measured individually. This enables us to formalize the tradeoff problem more rigorously. Secondly, we propose a privacy-utility tradeoff mechanism for that system. We apply a linear Gaussian model assumption to the system and thereby reduce the problem of obtaining unobservable information to that of learning the system parameters. Finally, we conduct experiments of applying the proposed mechanism to the power usage data of an actual household. The experimental results show that the proposed mechanism works partly effectively; i.e., it prevents usage analysis of certain types of sensitive appliances while at the same time preserving that of non-sensitive appliances.