Probabilistic programs are usual functional or imperative programs with two added
constructs:(1) the ability to draw values at random from distributions, and (2) the ability to …

Probabilistic inference is a key mechanism for reasoning about probabilistic programs.
Since exact inference is theoretically expensive, most probabilistic inference systems today …

Probabilistic programming systems (PP systems) allow developers to model stochastic
phenomena and perform efficient inference on the models. The number and adoption of …

We present a new algorithm for Bayesian inference over probabilistic programs, based on
data flow analysis techniques from the program analysis community. Unlike existing …

Probabilistic programming languages offer an intuitive way to model uncertainty by
representing complex probability models as simple probabilistic programs. Probabilistic …

Probabilistic programs use familiar notation of programming languages to specify
probabilistic models. Suppose we are interested in estimating the distribution of the return …

We show how static analysis for secure information flow can be expressed and proved
correct entirely within the framework of abstract interpretation. The key idea is to define a …

The trend constantly being observed in the evolution of advanced modern exploits is their
growing sophistication in stealthy attacks. Code-reuse attacks such as return-oriented …

In this paper we describe symbolic side-channel analysis techniques for detecting and
quantifying information leakage, given in terms of Shannon and min-entropy. Measuring the …

Databases can leak confidential information when users combine query results with
probabilistic data dependencies and prior knowledge. Current research offers mechanisms …