Real-Time and Spatio-Temporal Crowd-Sourced Social Network Data Publishing with Differential Privacy

Nowadays gigantic crowd-sourced data from mobile devices have become widely available in social networks, enabling the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of crowd-sourced social network data to the public will pose considerable threats to mobile users' privacy. In this paper, we investigate the problem of real-time spatio-temporal data publishing in social networks with privacy preservation. Specifically, we consider continuous publication of population statistics and design RescueDP-an online aggregate monitoring framework over infinite streams with omega-event privacy guarantee. Its key components including adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. Moreover, we further propose an enhanced RescueDP with neural networks to accurately predict the values of statistics and improve the utility of released data. Both RescueDP and the enhanced RescueDP are proved satisfying omega-event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two omega-event privacy-assured representative methods. Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.