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Secure Multi-Party Computation (MPC) allows mutually distrusting parties torun joint computations without revealing private data. Current MPC algorithmsscale poorly with data size, which makes MPC on "big data" prohibitively slowand inhibits its practical use.  Many relational analytics queries can maintain MPC's end-to-end securityguarantee without using cryptographic MPC techniques for all operations.Conclave is a query compiler that accelerates such queries by transforming theminto a combination of data-parallel, local cleartext processing and small MPCsteps. When parties trust others with specific subsets of the data, Conclaveapplies new hybrid MPC-cleartext protocols to run additional steps outside ofMPC and improve scalability further.  Our Conclave prototype generates code for cleartext processing in Python andSpark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclavescales to data sets between three and six orders of magnitude larger thanstate-of-the-art MPC frameworks support on their own. Thanks to its hybridprotocols, Conclave also substantially outperforms SMCQL, the most similarexisting system.

Conclave: secure multi-party computation on big data (extended TR)