Computer Science ›› 2022, Vol. 49 ›› Issue (12): 22-32.doi: 10.11896/jsjkx.220500240
• Federated Leaming • Previous Articles Next Articles
YANG Hong-jian, HU Xue-xian, LI Ke-jia, XU Yang, WEI Jiang-hong
CLC Number:
[1]ZHUANG M Q,TAN X H,FAN Y C,et al.3D animation expression generation and emotional supervision based on convolutional neural network[J].Journal of Chongqing University of Technology(Natural Science),2022,36(1):151-158. [2]WANG Z,GUO Y,NIE Z,et al.Privacy protection and cost management of smart meters based on dueling double deep Q-learning[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2021,33(4):554-561. [3]WANG J,XU Y H,LI L.Data fusion privacy protection method with low energy consumption and integrity verification[J].Journal of Jilin University(Engineering and Technology Edition),2022,52(7):1657-1665. [4]LI Q X,ZHOU Q X,WANG Z L,et al.Provable Secure Delegation Computing Protocol Based on Privacy Protection[J].Computer Engineering,2021,47(5):131-137. [5]YANG W Q,ZHANG Y,NIE J T,et al.Energy and Information Management Strategy Based on Federated Learning for Wireless Network Nodes[J].Computer Engineering,2022,48(1):188-196,203. [6]LIU Y X,CHEN H,LIU Y H,et al.Privacy-Preserving Techniques in Federated Learning [J].Ruan Jian Xue Bao/Journal of Software,2022,33(3):1057-1092. [7]WEN Y L,CHEN M J.Medical Data Sharing Scheme Combined with Federal Learning and Blockchain[J].Computer Enginee-ring,2022,48(5):145-153,161. [8]ZHU L,LIU Z,HAN S.Deep Leakage from Gradients [J].Advances in Neural Information Processing Systems,2019,32:1-11. [9]ZHAO B,MOPURI K R,BILEN H.iDLG:Improved Deep Lea-kage from Gradients [J].arXiv:2001.02610,2020. [10]WANG Z,SONG M,ZHANG Z,et al.Beyond Inferring Class Representatives:User-Level Privacy Leakage from Federated Learning[C]//IEEE INFOCOM 2019-IEEE Conference on Computer Communications.IEEE,2019:2512-2520. [11]BAKOPOULOU E,TILLMAN B,MARKOPOULOU A.AFederated Learning Approach for Mobile Packet Classification [J].arXiv:1907.13113,2019. [12]GE N,LI G H,ZHANG L,et al.Failure Prediction in Production Line Based on Federated Learning:An Empirical Study [J].arXiv:2101.11715,2021. [13]HARTMANN V,MODI K,PUJOL J M,et al.Privacy-Preserving Classification with Secret Vector Machines[C]//Procee-dings of the 29th ACM InternationalConference on Infor-mation & Knowledge Management.2020:475-484. [14]BURMESTER M,DESMEDT Y.A Secure and EfficientConfe-rence Key Distribution System[C]//Workshop on the Theory and Application of Cryptographic Techniques.Berlin:Springer,1994:275-286. [15]CHEON J H,KIM A,KIM M,et al.Homomorphic Encryption for Arithmetic of Approximate Numbers[C]//International Conference on the Theory and Application of Cryptology and Information Security.Cham:Springer,2017:409-437. [16]YU H,VAIDYA J,JIANG X.Privacy-Preserving SVM Classification on Vertically Partitioned Data[C]//Pacific-Asia Confe-rence on Knowledge Discovery and Data Mining.Berlin:Sprin-ger, 2006:647-656. [17]YU H,JIANG X,VAIDYA J.Privacy-Preserving SVM Using Nonlinear Kernels on Horizontally Partitioned Data[C]//Proceedings of the 2006 ACM Symposium on Applied Computing.2006:603-610. [18]VAIDYA J,YU H,JIANG X.Privacy-Preserving SVM Classification [J].Knowledge and Information Systems,2008,14(2):161-178. [19]MANGASARIAN O L,WILD E W.Privacy-Preserving Classification of Horizontally Partitioned Data via Random Kernels[C]//Proceedings of the 2008 International Conference on Data Mining.Las Vegas,USA,2008:473-479. [20]LEE Y J,MANGASARIAN O L.RSVM:Reduced Support Vector Machines[C]//Proceedings of the 2001 SIAM International Conference on Data Mining.Society for Industrial and Applied Mathematics.2001:1-17. [21]SUN L,MU W S,QI B,et al.A New Privacy-Preserving Proximal Support Vector Machine for Classification of Vertically Partitioned Data [J].International Journal of Machine Learning and Cybernetics,2015,6(1):109-118. [22]LIU X,DENG R H,CHOO K K R,et al.Privacy-PreservingOutsourced Support Vector Machine Design for Secure Drug Discovery [J].IEEE Transactions on Cloud Computing,2018,8(2):610-622. [23]LIU X,DENG R H,CHOO K K R,et al.An Efficient Privacy-Preserving Outsourced Calculation Toolkit with Multiple Keys [J].IEEE Transactions on Information Forensics and Security,2016,11(11):2401-2414. [24]WANG J,WU L,WANG H,et al.An Efficient and Privacy-Preserving Outsourced Support Vector Machine Training for Internet of Medical Things [J].IEEE Internet of Things Journal,2020,8(1):458-473. [25]MCMAHAN B,MOORE E,RAMAGE D,et al.Communic-ationEfficient Learning of Deep Networks from Decentralized Data[C]//Artificial Intelligence and Statistics.PMLR,2017:1273-1282. [26]RIVEST R L,ADLEMAN L,DERTOUZOS M L.On DataBanks and Privacy Homomorphisms [J].Foundations of Secure Computation,1978,4(11):169-180. [27]LYU L,YU H,YANG Q.Threats to Federated Learning:A Survey [J].arXiv:2003.02133,2020. [28]RAHIMI A,RECHT B.Random Features for Large-Scale Kernel Machines [J].Advances in Neural Information Processing Systems,2007,20:1177-1184. [29]RUDIN W.Fourier Analysis on Groups[M].New York:Courier Dover Publications,2017. [30]GREGORY G.Predicts Random Fourier Features[EB/OL].(2019-12-23) [2022-05-24].http://gregorygundersen.com/blog/2019/12/23/random-fourier-features/. [31]CHEON J H,HONG S,KIM D.Remark on the Security ofCKKS Scheme in Practice [EB/OL].(2020-12-21) [2022-05-26].https://eprint.iacr.org/2020/1581.pdf. [32]ODED G.Foundations of Cryptography-Basic Applications[M].Cambridge:Cambridge University Press,2004. [33]BOST R,POPA R A,TU S,et al.Machine Learning Classification over Encrypted Data[C]//Network and Distributed System Security Symposium.2014. |
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