DPSD: Dynamic Private Spatial Decomposition Based on Spatial and Temporal Correlations

Abstract

IoT data collected in a physical space have intrinsic values, such as the spatial and temporal correlation of people’s activities. As such spatio-temporal data inevitably includes private information like trajectory and waypoints, privacy exposure becomes a serious problem. Local Differential Privacy (LDP) has been gaining attention as a privacy protection procedure on a device collecting spatio-temporal data. However, LDP cannot retain spatial and temporal properties which are essential for cyber-physical systems. The is because LDP makes each data indistinguishable and inevitably removes spatial and temporal properties as well. In this paper, we propose a method enabling LDP to keep spatial and temporal properties on privacy protection process. Our method dynamically changes the strength of privacy protection (called privacy budget) for each of device groups who has resemble spatial and temporal behavior. This makes data of each device in a group indistinguishable within the group but a set of data made by a group distinguishable between groups in terms of spatial and temporal domains. As the whole data merged in a data store will consists of modified data with wide variety of privacy budgets, we arrange every privacy budgets so that merged data keeps particular strength of privacy protection. We call this process as Dynamic Private Spatial Decomposition (DPSD). The experimental results show that our LDP preserves the data utility while maintaining the privacy protection of the entire client because of DPSD.