Knowledge-oriented secure multiparty computation
Proceedings of the 7th Workshop on Programming Languages and Analysis for Security
A model-learner pattern for bayesian reasoning
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation
A framework for using trust to assess risk in information sharing
AT'13 Proceedings of the Second international conference on Agreement Technologies
Tabular: a schema-driven probabilistic programming language
Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
Dynamic enforcement of knowledge-based security policies using probabilistic abstract interpretation
Journal of Computer Security
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This paper explores the idea of knowledge-based security policies, which are used to decide whether to answer queries over secret data based on an estimation of the querier's (possibly increased) knowledge given the results. Limiting knowledge is the goal of existing information release policies that employ mechanisms such as noising, anonymization, and redaction. Knowledge-based policies are more general: they increase flexibility by not fixing the means to restrict information flow. We enforce a knowledge-based policy by explicitly tracking a model of a querier's belief about secret data, represented as a probability distribution, and denying any query that could increase knowledge above a given threshold. We implement query analysis and belief tracking via abstract interpretation using a novel probabilistic polyhedral domain, whose design permits trading off precision with performance while ensuring estimates of a querier's knowledge are sound. Experiments with our implementation show that several useful queries can be handled efficiently, and performance scales far better than would more standard implementations of probabilistic computation based on sampling.