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Probabilistic computation is a convenient means of mechanically reasoning about a variety of information security problems. At its core, information security concerns itself with measuring or limiting the knowledge an adversary might attain from interacting with a protected system. Probabilistic inference lets one compute this knowledge explicitly as long as the interaction can be described as a program in a suitable probabilistic language that supports conditioning. Security concerns, however, require soundness guarantees on probabilistic inference which are generally not present in machine learning applications. We summarize some recent work on probabilistic computing for information security and highlight challenging aspects that still need to be addressed.
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