随笔分类 - 【五期李伟平】
摘要：Nagalapatti, Lokesh , and R. Narayanam . "Game of Gradients: Mitigating Irrelevant Clients in Federated Learning." (2021). 针对联邦学习中相关客户端选择（FRCS）的问题，本文提
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摘要：Zafari, Faheem , et al. "A Game-Theoretic Approach to Multi-Objective Resource Sharing and Allocation in Mobile Edge Clouds." (2018). 为了缓解移动边缘计算中资源稀缺问
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摘要：Peng Wu, Fengen Li, Jie Zhao, et al. Consensus Reaching Process With Multiobjective Optimization for Large-Scale Group Decision Making With Cooperativ
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摘要：Zhang, Ning , Q. Ma , and X. Chen . "Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective." (2022). 针对重复执行跨筒仓联
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摘要：Nan W., et al. “The Value of Collaboration in Convex Machine Learning with Differential Privacy.” 2020 IEEE Symposium on Security and Privacy. 304-317
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摘要：Xin, S. , et al. "Feature evaluation and selection with cooperative game theory." Pattern Recognition 45.8(2012):2992-3002. 基于合作博弈寻找最优特征子集，重点解决传统基于信息论
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摘要：Bao, Xianglin , et al. "FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive." 2019 5th International Conference on Big Dat
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摘要：Song, F. , et al. "Efficient and Secure k-Nearest Neighbor Search Over Encrypted Data in Public Cloud." ICC 2019 - 2019 IEEE International Conference
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摘要：Luo, et al. "Outlier-eliminated k-means clustering algorithm based on differential privacy preservation." Applied Intelligence the International Journ
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摘要：Mugunthan, V. , A. Peraire-Bueno , and L. Kagal . "PrivacyFL: A simulator for privacy-preserving and secure federated learning.", 10.1145/3340531.3412
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摘要：Thapa, C. , M. Chamikara , and S. Camtepe . "SplitFed: When Federated Learning Meets Split Learning." (2020). 本文提出了一种联邦学习（FL）和分割学习（SL）的混合方法（SFL），能够同时解
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摘要：Rindal, Peter , and P. Schoppmann . "VOLE-PSI: Fast OPRF and Circuit-PSI from Vector-OLE." 2021. 本文采用VOLE和PaXoS数据结构提出了一种批处理伪随机数函数OPRF的构造，并用其实现隐私交集PSI。
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摘要：Li, X. , R. Dowsley , and MD Cock. "Privacy-Preserving Feature Selection with Secure Multiparty Computation.", 10.48550/arXiv.2102.03517. 2021. 当前PPML
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摘要：Kolesnikov, V. , et al. "Practical Multi-party Private Set Intersection from Symmetric-Key Techniques." Acm Sigsac Conference on Computer & Communicat
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摘要：Qz, A , et al. "Blockchain-based privacy-preserving remote data integrity checking scheme for IoT information systems - ScienceDirect." Information Pr
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摘要：Ofri Nevo, Ni Trieu and Avishay Yanai. "Simple, Fast Malicious Multiparty Private Set Intersection." In Proceedings of the 2021 ACM SIGSAC Conference
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摘要：Jun Liu, Yuan Tian, Yu Zhou et al. "Privacy preserving distributed data mining based on secure multi-party computation." Computer Communications. Ed.
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摘要：Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, Anderson Nascimento. 2019. Privacy-Preserving Classification of Personal Text Messages wit
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摘要：Jonas Böhler and Florian Kerschbaum. 2020. Secure Multi-party Computation of Differentially Private Median. In the Proceedings of the 29th USENIX Secu
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摘要：Jonas Böhler and Florian Kerschbaum. 2021. Secure Multi-party Computation of Differentially Private Heavy Hitters. In Proceedings ofthe 2021 ACM SIGSA
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