ANOSY: approximated knowledge synthesis with refinement types for declassification
ANOSY: approximated knowledge synthesis with refinement types for declassification
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ANOSY:具有用于解密的细化类型的近似知识合成
DOI:
10.1145/3519939.3523725
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发表时间:
2022
期刊:
影响因子:
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通讯作者:
Parker, James
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文献类型:
--
作者:
Guria, Sankha Narayan;Vazou, Niki;Guarnieri, Marco;Parker, James
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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