Senate: A Maliciously-Secure MPC Platform for Collaborative Analytics
Rishabh Poddar, Sukrit Kalra, Avishay Yanai, Ryan Deng, Raluca Ada Popa, Joseph M. Hellerstein
摘要
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.
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引用它的顶会 Paper31
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- Secure Yannakakis: Join-Aggregate Queries over Private DataYilei Wang, Ke YiSIGMOD 2021 · 被引用 49 次
- HEDA: Multi-Attribute Unbounded Aggregation over Homomorphically Encrypted DatabaseXuanle Ren, Le Su, Zhen Gu, Sheng Wang 等VLDB 2023 · 被引用 42 次
- MAGE: Nearly Zero-Cost Virtual Memory for Secure ComputationSam Kumar, David E. Culler, Raluca Ada PopaOSDI 2021 · 被引用 24 次
它引用的顶会 Paper12
- Practical Secure Aggregation for Privacy-Preserving Machine LearningKallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone 等CCS 2017 · 被引用 3,936 次
- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 被引用 2,107 次
- Foreshadow: Extracting the Keys to the Intel SGX Kingdom with Transient Out-of-Order ExecutionJo Van Bulck, Marina Minkin, Ofir Weisse, Daniel Genkin 等USENIX Security 2018 · 被引用 1,175 次
- MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious TransferMarcel Keller, Emmanuela Orsini, Peter SchollCCS 2016 · 被引用 487 次
- Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGXWenhao Wang, Guoxing Chen, Xiaorui Pan, Yinqian Zhang 等CCS 2017 · 被引用 403 次
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