Social-Aware Privacy-Preserving Correlated Data Collection

We study a privacy-preserving data collection problem, by jointly considering data reporters' data correlation and social relationship. A data collector gathers data from individuals to perform a certain analysis with a privacy-preserving mechanism. Due to data correlation, the data analysis based on the reported data can cause privacy leakage to other individuals (even if they do not report data). The data reporters will take such a privacy threat into account, owing to the social relationship among individuals. This motivates us to formulate a two-stage Stackelberg game: In Stage I, the data collector selects some individuals as data reporters and designs a privacy-preserving mechanism for a sum query analysis. In Stage II, the selected data reporters contribute their data with possible perturbations (through adding noise). By analyzing the data reporters' equilibrium decisions in Stage II, we show that given any fixed reporter set, only one data reporter with the most significant joint consideration of the social relationship and data correlation may add noise to his reported data. The rest of the data reporters will truthfully report their data. In Stage I, we derive the data collector's optimal privacy-preserving mechanism and propose an efficient algorithm to select the data reporters. We conclude that the data collector should jointly capture the impact of data correlation and social relation to ensure all data reporters truthfully reporting their data. We conduct extensive simulations based on random network and real-world social data to investigate the impact of data correlation and social network on the system. We find that the availability of social network information is more critical to the data collector compared with data correlation information.