Differential privacy is a de facto standard for statistical computations over databases that
contain private data. Its main and rather surprising strength is to guarantee individual privacy …

We study the complexity of the problem of verifying differential privacy for while-like
programs working over boolean values and making probabilistic choices. Programs in this …

Differential privacy is a widely studied notion of privacy for various models of computation,
based on measuring differences between probability distributions. We consider (epsilon …

We study the problem of verifying differential privacy for loop-free programs with probabilistic
choice. Programs in this class can be seen as randomized Boolean circuits, which we will …

A cyber-physical system (CPS) is expected to be resilient to more than one type of
adversary. In this paper, we consider a CPS that has to satisfy a linear temporal logic (LTL) …

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 …

Differential privacy (DP) is a statistical definition of privacy which ensures that the outcome of
a computation by an analyst only depends in a negligible way on the presence of a single …

Differential privacy is a de facto standard for statistical computations over databases that
contain private data. The strength of differential privacy lies in a rigorous mathematical …

This thesis contributes to the understanding of the computational complexity of verifying
differential privacy. The problem is considered in two constrained, but expressive, models; …

With the explosion of digital data collected from social apps, privacy protection regulations
have been issued by almost all countries. Differential privacy is proposed as a successful …