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 …

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 …

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 …

Differential privacy is a mathematical framework for developing statistical computations with
provable guarantees of privacy and accuracy. In contrast to the privacy component of …

Differential privacy is a widely studied notion of privacy for various models of computation.
Technically, it is based on measuring differences between probability distributions. We study …

Differential privacy is a promising formal approach to data privacy, which provides a
quantitative bound on the privacy cost of an algorithm that operates on sensitive information …

Differential privacy has emerged as a promising probabilistic formulation of privacy,
generating intense interest within academia and industry. We present a push-button …

Applying differential privacy at scale requires convenient ways to check that programs
computing with sensitive data appropriately preserve privacy. We propose here a fully …

We investigate the integration of two approaches to information security: information flow
analysis, in which the dependence between secret inputs and public outputs is tracked …

Over the last decade, differential privacy has achieved widespread adoption within the
privacy community. Moreover, it has attracted significant attention from the verification …