Online Social networks provide a new way of accessing and collectively treating information. Their efficiency is critically predicated on the quality of information provided, the ability of users to assess such quality, and to connect to like-minded users to exchange useful content.

To improve this efficiency, we develop mechanisms for assessing users’ expertise and recommending suitable content. We further develop algorithms for identifying latent user communities and recommending potential contacts to users.

Users’ personal data in general and online activity in particular should be protected from privacy threats. On the other hand it is valuable for conducting various inferences and supporting personalized services. We develop methods for performing such inferences on private data kept under the users’ control, while preserving their privacy through combinations of data distortion, encryption, and anonymization.