Privacy, applied

Internet users may have compelling reasons to seek anonymity online, for example, to discuss stigmatizing issues with others like themselves, or to express dissident opinions. This project studies what people believe it means to be anonymous online, how their privacy and security are affected by their strategies to achieve anonymity, and how they are likely to use new anonymity services. These questions are important because the traceability of users? actions across sites and contexts is ever greater, increasing risks for users who may misjudge their actual anonymity.

Database Management Systems (DBMS) have been used to store and process data in organizations for decades. Larger organizations use a variety of databases (commercial, open-source or custom-built) for different departments. However, neither users nor Database Administrators (DBAs) know exactly where the data is stored on the system or how it is processed. Most relational databases store internal data using universal principles that can be inferred and captured.

In order to enable collaboration between different parties it is necessary that the partners reach an agreement on the policy rules that will govern their interaction. While state-of-the-art mechanisms will allow the parties to reconcile their polices, today's policy reconciliation protocols have two main shortcomings. First, they violate privacy since at least one of the parties is required to discloses all its information during the reconciliation process. Second, they generally lack fairness, i.e., the parties' preferences are not recognized.

Youths of the digital age live parallel lives online and in the real world, frequently disclosing personal information to cyberfriends and strangers, regardless of race, class or gender. Race and gender do make a difference, however, when these online disclosures lead to acts of cyberaggression. The PIs' previous work revealed that some youths are resistant to cyberaggression and that there are differences in perceptions of cyberbullying among youths from different cultural and racial backgrounds.

Informally speaking, Secure Multi-Party Computation (SMPC) allows two or more parties to jointly compute some function on their private inputs in a distributed fashion (i.e., without the involvement of a trusted third party) such that none of the parties learns anything beyond its dedicated output and what it can deduce from considering both this output and its own private input. Since its inception in 1982 by Yao, SMPC has advanced greatly and over the years a large body of work has been developed.