Coupled Relational Symbolic Execution for Differential Privacy

Gian Pietro Farina, Stephen Chong, Marco Gaboardi

Abstract: Differential privacy is a de facto standard in data privacy with applications in the private and public sectors. Most of the techniques that achieve differential privacy are based on a judicious use of randomness. However, reasoning about randomized programs is difficult and error prone. For this reason, several techniques have been recently proposed to support designer in proving programs differentially private or in finding violations to it.