Conclave: secure multi-party computation on big data (extended TR)
This addresses the scalability bottleneck for MPC in big data applications, enabling practical use in scenarios like secure data analytics, though it is incremental as it builds on existing MPC frameworks.
The paper tackles the problem of scaling secure multi-party computation (MPC) for big data by introducing Conclave, a query compiler that accelerates relational analytics queries through a combination of local cleartext processing and small MPC steps, achieving scalability to data sets three to six orders of magnitude larger than state-of-the-art MPC frameworks.
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.