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

• Published in: IEEE transactions on dependable and secure computing Vol. 15; no. 4; pp. 591 - 606
• Main Authors: Wang, Qian, Zhang, Yan, Lu, Xiao, Wang, Zhibo, Qin, Zhan, Ren, Kui
• Format: Journal Article
• Language: English
• Published: Washington IEEE 01.07.2018; IEEE Computer Society
• Subjects: Adaptive filters; Adaptive sampling; Computer privacy; Crowd-sourced data; Crowdsourcing; Data mining; Data privacy; Data retrieval; differential privacy; Electronic devices; Mobile handsets; Neural networks; Perturbation; Population statistics; Privacy; privacy preservation; Publishing; Real time; Real-time systems; realtime data publishing; Social network services; Social networks; Statistics; Time series analysis
• ISSN: 1545-5971; 1941-0018
• DOI: 10.1109/TDSC.2016.2599873

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 <inline-formula> <tex-math notation="LaTeX">w</tex-math> <inline-graphic xlink:href="wang-ieq1-2599873.gif"/> </inline-formula>-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 <inline-formula><tex-math notation="LaTeX">w</tex-math> <inline-graphic xlink:href="wang-ieq2-2599873.gif"/> </inline-formula>-event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two <inline-formula> <tex-math notation="LaTeX">w</tex-math> <inline-graphic xlink:href="wang-ieq3-2599873.gif"/> </inline-formula>-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.