Social Computing Systems

Individuals sharing data on today’s social computing systems face privacy losses due to information disclosure that go much beyond the data they directly share, as it is possible to infer additional information about a user from data shared by other users. Users have identities across several social computing systems and reveal different aspects of their lives in each. When considering multiple social computing systems, information disclosure can be of two types: attribute disclosure or identity disclosure. These disclosure risks intimately rely on matching the profiles that belong to a same individual on several systems. This raises many key questions: how to match profiles at very large scale and what are the limits? how to measure and quantify the two privacy risks and how do they relate to each other? At EURECOM, we tackle the problem of quantifying attribute and identity disclosure risks across multiple social computing systems. Topics include:

  • Identity matching at very large scale

  • Disclosure risks 

  • Social networks


Selected publications: 

  • O. Goga, P. Loiseau, R. Sommer, R. Teixeira, and K. Gummadi. On the reliability of profile matching across large online social networks. In Proceedings of the 21st ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD), 2016. [ bib | pdf | http ]


Syndicate content

Data Science