Dynamic Enforcement of Dierential Privacy

With recent privacy failures in the release of personal data, dieren-tial privacy received considerable attention in the research community. This mathematical concept, despite its young age (Dwork, 2006), has grabbed the attention of many researchers for its robustness against identication of individuals even in presence of background informa-tion. Beside that, its exible denition makes it compatible with dif-ferent data sources, data mining algorithms and data release models. Its compositionality properties facilitates design of “dierential privacy aware ” programming languages. These programming languages and frameworks empower non-experts to construct complex data mining analyses with proven dierential privacy guarantees. This thesis mostly focuses on two aspects of such systems: proving correctness of these frameworks, and improving their accuracy. The correctness is impor-tant since some frameworks (like PINQ) derivates from theory withou