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

arXiv: https://arxiv.org/abs/1902.06288 · HTML: https://mlsanity.com/paper/1902.06288/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Machine Learning with Confidential Computing: A Systematization of Knowledge](https://mlsanity.com/paper/2208.10134/) (2022)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [A Survey of Secure Computation Using Trusted Execution Environments](https://mlsanity.com/paper/2302.12150/) (2023)
- [Prism: Private Verifiable Set Computation over Multi-Owner Outsourced Databases](https://mlsanity.com/paper/2104.03354/) (2021)
- [Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support](https://mlsanity.com/paper/2103.16898/) (2021)
- [Attention Paper: How Generative AI Reshapes Digital Shadow Industry?](https://mlsanity.com/paper/2305.18346/) (2023)

## Abstract (arXiv)

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.