Secret-Shared Shuffle with Malicious Security
Xiangfu Song, Dong Yin, Jianli Bai, Changyu Dong, Ee-Chien Chang
摘要
—A secret-shared shuffle (SSS) protocol permutes a secret-shared vector using a random secret permutation. It has found numerous applications, however, it is also an expensive operation and often a performance bottleneck. Chase et al. (Asiacrypt’20) recently proposed a highly efficient semi-honest two-party SSS protocol known as the CGP protocol. It utilizes purposely designed pseudorandom correlations that facilitate a communication-efficient online shuffle phase. That said, semi-honest security is insufficient in many real-world application scenarios since shuffle is usually used for highly sensitive applications. Considering this, recent works (CANS’21, NDSS’22) attempted to enhance the CGP protocol with malicious security over authenticated secret sharings. However, we find that these attempts are flawed, and malicious adversaries can still learn private information via malicious deviations. This is demonstrated with concrete attacks proposed in this paper. Then the question is how to fill the gap and design a maliciously secure CGP shuffle protocol. We answer this question by introducing a set of lightweight correlation checks and a leakage reduction mechanism. Then we apply our techniques with authenticated secret sharings to achieve malicious security. Notably, our protocol, while increasing security, is also efficient. In the two-party setting, experiment results show that our maliciously secure protocol introduces an acceptable overhead compared to its semi-honest version and is more efficient than the state-of-the-art maliciously secure SSS protocol from the MP-SPDZ library.
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引用它的顶会 Paper5
- Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential PrivacyShuangqing Xu, Yifeng Zheng, Zhongyun HuaCCS 2024 · 被引用 5 次
- Piquant: Private Quantile Estimation in the Two-Server ModelHannah Keller, Jacob Imola, Fabrizio Boninsegna, Rasmus Pagh 等CCS 2026
- FLOSS: Fast Linear Online Secret-Shared ShufflingIan Chang, Sela Navot, Alex Ozdemir, Nirvan TyagiUSENIX Security 2026
- Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data AnalyticsShuyu Chen, Mingxun Zhou, Haoyu Niu, Guopeng Lin 等VLDB 2026
- Ring of Gyges: Accountable Anonymous Broadcast via Secret-Shared ShuffleWentao Dong, Peipei Jiang, Huayi Duan, Cong Wang 等NDSS 2025
它引用的顶会 Paper20
- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 被引用 2,107 次
- MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious TransferMarcel Keller, Emmanuela Orsini, Peter SchollCCS 2016 · 被引用 487 次
- Efficient Two-Round OT Extension and Silent Non-Interactive Secure ComputationElette Boyle, Geoffroy Couteau, Niv Gilboa, Yuval Ishai 等CCS 2019 · 被引用 238 次
- Optimized Honest-Majority MPC for Malicious Adversaries - Breaking the 1 Billion-Gate Per Second BarrierToshinori Araki, Assi Barak, Jun Furukawa, Tamar Lichter 等S&P 2017 · 被引用 137 次
- Revisiting Square-Root ORAM: Efficient Random Access in Multi-party ComputationSamee Zahur, Xiao Wang, Mariana Raykova, Adrià Gascón 等S&P 2016 · 被引用 124 次
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