STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Distributed Private Data Analysis: Simultaneously Solving How and What
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
Quantifying information flow with beliefs
Journal of Computer Security - 18th IEEE Computer Security Foundations Symposium (CSF 18)
Faster secure two-party computation using garbled circuits
SEC'11 Proceedings of the 20th USENIX conference on Security
Dynamic Enforcement of Knowledge-Based Security Policies
CSF '11 Proceedings of the 2011 IEEE 24th Computer Security Foundations Symposium
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
On protection in federated social computing systems
Proceedings of the 4th ACM conference on Data and application security and privacy
Dynamic enforcement of knowledge-based security policies using probabilistic abstract interpretation
Journal of Computer Security
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Protocols for secure multiparty computation (SMC) allow a set of mutually distrusting parties to compute a function f of their private inputs while revealing nothing about their inputs beyond what is implied by the result. Depending on f, however, the result itself may reveal more information than parties are comfortable with. Almost all previous work on SMC treats f as given. Left unanswered is the question of how parties should decide whether it is "safe" for them to compute f in the first place. We propose here a way to apply belief tracking to SMC in order to address exactly this question. In our approach, each participating party is able to reason about the increase in knowledge that other parties could gain as a result of computing f, and may choose not to participate (or participate only partially) so as to restrict that gain in knowledge. We develop two techniques---the belief set method and the SMC belief tracking method---prove them sound, and discuss their precision/performance tradeoffs using a series of experiments.