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.

In this work we propose a technique based on symbolic execution for reasoning about differential privacy. Symbolic execution is a classic technique used for testing, counterexample generation and to prove absence of bugs. Here we use symbolic execution to support these tasks specifically for differential privacy. To achieve this goal, we design a relational symbolic execution technique which supports reasoning about probabilistic coupling, a formal notion that has been shown useful to structure proofs of differential privacy. We show how our technique can be used to both verify and find violations to differential privacy.

Coupled Relational Symbolic Execution for Differential Privacy

ETAPS is a confederation of several conferences, each with its own Programme Committee and Steering Committee. ETAPS is the most important and visible annual European event related to software sciences. Altogether, more than 500 researchers participate in this event every year. The confederated conferences cover various aspects of software systems, ranging from theoretical foundations to programming language developments, compiler advancements, analysis tools, formal approaches to software engineering, and security.

A gather.town room will be open during the whole conference, which can be found here:

https://gather.town/app/nSgVv0DDYoCkmdkP/ETAPS2021

Password: Luxembourg2021

This can be used to meet in the coffee breaks.

 PDF's of the papers are available on https://etaps.org/2021/programme