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

## TL;DR

Conclave is a query compiler that enhances secure multi-party computation on big data by combining data-parallel processing with hybrid MPC protocols, significantly improving scalability and performance over existing systems.

## Contribution

It introduces a hybrid approach that transforms relational queries into a mix of cleartext and MPC steps, enabling scalable secure computation on large datasets.

## Key findings

- Scales to datasets 3-6 orders of magnitude larger than existing MPC frameworks.
- Outperforms SMCQL in scalability and efficiency.
- Supports code generation for Python, Spark, and MPC frameworks.

## Abstract

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.