PG: Byzantine Fault-Tolerant and Privacy-Preserving Sensor Fusion with Guaranteed Output Delivery
Chenglu Jin, Chao Yin, Marten van Dijk, Sisi Duan, Fabio Massacci, Michael K. Reiter, Haibin Zhang
摘要
We design and implement PG, a Byzantine fault-tolerant and privacypreserving multi-sensor fusion system. PG is flexible and extensible, supporting a variety of fusion algorithms and application scenarios. On the theoretical side, PG develops and unifies techniques from dependable distributed systems and modern cryptography. PG can provably protect the privacy of individual sensor inputs and fusion results. In contrast to prior works, PG can provably defend against pollution attacks and guarantee output delivery, even in the presence of malicious sensors that may lie about their inputs, contribute ill-formed inputs, and provide no inputs at all to sway the final result, and in the presence of malicious servers serving as aggregators. On the practical side, we implement PG in the client-serversensor setting. Moreover, we deploy PG in a cloud-based system
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper10
- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 被引用 2,107 次
- Global-Scale Secure Multiparty ComputationXiao Wang, Samuel Ranellucci, Jonathan KatzCCS 2017 · 被引用 220 次
- Efficient Private Statistics with Succinct SketchesLuca Melis, George Danezis, Emiliano De CristofaroNDSS 2016 · 被引用 128 次
- Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated LearningJinhyun So, Ramy E. Ali, Basak Güler, Jiantao Jiao 等AAAI 2023 · 被引用 107 次
- The Fundamental Price of Secure Aggregation in Differentially Private Federated LearningWei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha SureshICML 2022 · 被引用 82 次
相关 Paper
- Byzantine-tolerant federated Gaussian process regression for streaming dataXu Zhang, Zhenyuan Yuan, Minghui ZhuNeurIPS 2022 · 被引用 7 次
- Practical Differentially Private and Byzantine-resilient Federated LearningZihang Xiang, Tianhao Wang, Wanyu Lin, Di WangSIGMOD 2023 · 被引用 22 次
- AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated LearningDong Chen, Hongyuan Qu, Guangwu XuICML 2024 · 被引用 8 次
- zPROBE: Zero Peek Robustness Checks for Federated LearningZahra Ghodsi, Mojan Javaheripi, Nojan Sheybani, Xinqiao Zhang 等ICCV 2023 · 被引用 27 次
- RoboRebound: Multi-Robot System Defense with Bounded-Time InteractionNeeraj Gandhi, Yifan Cai, Andreas Haeberlen, Linh Thi Xuan PhanEuroSys 2025 · 被引用 1 次
