10,663 research outputs found
Combining Private Set-Intersection with Secure Two-Party Computation
Private Set-Intersection (PSI) is one of the most popular and practically relevant secure two-party computation (2PC) tasks. Therefore, designing special-purpose PSI protocols (which are more efficient than generic 2PC solutions) is a very active line of research. In particular, a recent line of work has proposed PSI protocols based on oblivious transfer (OT) which, thanks to recent advances in OT-extension techniques, is nowadays a very cheap cryptographic building block.
Unfortunately, these protocols cannot be plugged into larger 2PC applications since in these protocols one party (by design) learns the output of the intersection. Therefore, it is not possible to perform secure post-processing of the output of the PSI protocol.
In this paper we propose a novel and efficient OT-based PSI protocol that produces an encrypted output that can therefore be later used as an input to other 2PC protocols. In particular, the protocol can be used in combination with all common approaches to 2PC including garbled circuits, secret sharing and homomorphic encryption. Thus, our protocol can be combined with the right 2PC techniques to achieve more efficient protocols for computations of the form for arbitrary functions
Conclave: secure multi-party computation on big data (extended TR)
Secure Multi-Party Computation (MPC) allows mutually distrusting parties to
run joint computations without revealing private data. Current MPC algorithms
scale poorly with data size, which makes MPC on "big data" prohibitively slow
and inhibits its practical use.
Many relational analytics queries can maintain MPC's end-to-end security
guarantee without using cryptographic MPC techniques for all operations.
Conclave is a query compiler that accelerates such queries by transforming them
into a combination of data-parallel, local cleartext processing and small MPC
steps. When parties trust others with specific subsets of the data, Conclave
applies new hybrid MPC-cleartext protocols to run additional steps outside of
MPC and improve scalability further.
Our Conclave prototype generates code for cleartext processing in Python and
Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave
scales to data sets between three and six orders of magnitude larger than
state-of-the-art MPC frameworks support on their own. Thanks to its hybrid
protocols, Conclave also substantially outperforms SMCQL, the most similar
existing system.Comment: Extended technical report for EuroSys 2019 pape
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