Senate: A Maliciously-Secure MPC Platform for Collaborative Analytics
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Abstract (arXiv)
Many organizations stand to benefit from pooling their data together in order to draw mutually beneficial insights -- e.g., for fraud detection across banks, better medical studies across hospitals, etc. However, such organizations are often prevented from sharing their data with each other by privacy concerns, regulatory hurdles, or business competition. We present Senate, a system that allows multiple parties to collaboratively run analytical SQL queries without revealing their individual data to each other. Unlike prior works on secure multi-party computation (MPC) that assume that all parties are semi-honest, Senate protects the data even in the presence of malicious adversaries. At the heart of Senate lies a new MPC decomposition protocol that decomposes the cryptographic MPC computation into smaller units, some of which can be executed by subsets of parties and in parallel, while preserving its security guarantees. Senate then provides a new query planning algorithm that decomposes and plans the cryptographic computation effectively, achieving a performance of up to 145× faster than the state-of-the-art.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1.
3 papers.
- 2023Federated Analytics: A surveycited by 9
- 2021Separation of Powers in Federated Learningcited by 8
- 2022MPClan: Protocol Suite for Privacy-Conscious Computationscited by 1
- 2023Federated Analytics: A survey2 Feb 2023
- 2022MPClan: Protocol Suite for Privacy-Conscious Computations24 Jun 2022
- 2021Separation of Powers in Federated Learning19 May 2021