Conclave: Secure Multi-Party Computation on Big 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 and additional optimizations, Conclave also substantially outperforms SMCQL, the most similar existing system.