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arXiv · 2203.12069

ANOSY: Approximated Knowledge Synthesis with Refinement Types for Declassification

Sankha Narayan Guria·Niki Vazou·Marco Guarnieri·James Parker
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Abstract

Non-interference is a popular way to enforce confidentiality of sensitive data. However, declassification of sensitive information is often needed in realistic applications but breaks non-interference. We present ANOSY, an approximate knowledge synthesizer for quantitative declassification policies. ANOSY uses refinement types to automatically construct machine checked over- and under-approximations of attacker knowledge for boolean queries on multi-integer secrets. It also provides an AnosyT monad to track the attacker knowledge over multiple declassification queries and checks for violations against user-specified policies in information flow control applications. We implement a prototype of ANOSY and show that it is precise and permissive: up to 14 declassification queries are permitted before a policy violation occurs using the powerset of intervals domain.

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Sankha Narayan Guria, Niki Vazou, Marco Guarnieri, James Parker. 2022-03-22. ANOSY: Approximated Knowledge Synthesis with Refinement Types for Declassification. https://arxiv.org/abs/2203.12069

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