Search

Summary

Bayesian probability models uncertain knowledge and learning from observations. As a defining feature of optimal adversarial behaviour, Bayesian reasoning forms the basis of safety properties in contexts such as privacy and fairness. Probabilistic programming is a convenient implementation of Bayesian reasoning but the adversarial setting imposes obstacles to its use: approximate inference can underestimate adversary knowledge and exact inference is impractical in cases covering large state spaces. By abstracting distributions, the semantics of a probabilistic language, and inference, jointly termed probabilistic abstract interpretation, we demonstrate adversary models both approximate and sound. We apply the techniques to build a privacy-protecting monitor and describe how to trade off the precision and computational cost in its implementation while remaining sound with respect to privacy risk bounds.

Search Results

Refine search

Refine search

Actions for selected content:

1 results

11 - Probabilistic Abstract Interpretation: Sound Inference and Application to Privacy

Summary

Search Results

Refine search

Refine search

Actions for selected content:

Save Search

1 results

11 - Probabilistic Abstract Interpretation: Sound Inference and Application to Privacy

Summary