Malte Schwarzkopf, Massachusetts Institute of Technology

Abstract

Current algorithms for secure multi-party computation (MPC) scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use. In this work, we set out to both address this problem and to, at the same time, make MPC more accessible for data analysts who are unfamiliar with current MPC frameworks.

Many 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 and additional optimizations, Conclave also substantially outperforms SMCQL, the most similar existing system.

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