Secure Data Assimilation of Cloud Sensor Networks
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[1] Xianbin Wang,et al. Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions , 2018, IEEE Communications Surveys & Tutorials.
[2] Philip S. Yu,et al. Multi-View Fusion with Extreme Learning Machine for Clustering , 2019, ACM Trans. Intell. Syst. Technol..
[3] Qiang Yang,et al. Federated Machine Learning , 2019, ACM Trans. Intell. Syst. Technol..
[4] Quanyan Zhu,et al. Modeling, Analysis, and Mitigation of Dynamic Botnet Formation in Wireless IoT Networks , 2018, IEEE Transactions on Information Forensics and Security.
[5] Quanyan Zhu,et al. A Game-Theoretic Approach to Design Secure and Resilient Distributed Support Vector Machines , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[6] Quanyan Zhu,et al. Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs , 2018, IEEE Transactions on Signal and Information Processing over Networks.
[7] Quanyan Zhu,et al. Consensus-based transfer linear support vector machines for decentralized multi-task multi-agent learning , 2018, 2018 52nd Annual Conference on Information Sciences and Systems (CISS).
[8] Quanyan Zhu,et al. A game-theoretic defense against data poisoning attacks in distributed support vector machines , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).
[9] Quanyan Zhu,et al. Optimizing mission critical data dissemination in massive IoT networks , 2017, 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).
[10] Rui Zhang,et al. A game-theoretic analysis of label flipping attacks on distributed support vector machines , 2017, 2017 51st Annual Conference on Information Sciences and Systems (CISS).
[11] Quanyan Zhu,et al. Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning , 2017, IEEE Transactions on Information Forensics and Security.
[12] Takahiro Fujita,et al. Cyber-security enhancement of networked control systems using homomorphic encryption , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).
[13] Miriam A. M. Capretz,et al. MLaaS: Machine Learning as a Service , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).
[14] Quanyan Zhu,et al. Environment-aware power generation scheduling in smart grids , 2015, 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm).
[15] Rui Zhang,et al. Secure and resilient distributed machine learning under adversarial environments , 2015, 2015 18th International Conference on Information Fusion (Fusion).
[16] Alexander J. Smola,et al. Communication Efficient Distributed Machine Learning with the Parameter Server , 2014, NIPS.
[17] Quanyan Zhu,et al. Dynamic Service Placement in Geographically Distributed Clouds , 2012, IEEE Journal on Selected Areas in Communications.
[18] Mauro Barni,et al. Encrypted signal processing for privacy protection: Conveying the utility of homomorphic encryption and multiparty computation , 2013, IEEE Signal Processing Magazine.
[19] Yong Tang,et al. Trusted Data Sharing over Untrusted Cloud Storage Providers , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.
[20] Xiaojun Wu,et al. DAvinCi: A cloud computing framework for service robots , 2010, 2010 IEEE International Conference on Robotics and Automation.
[21] Raouf Boutaba,et al. Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.
[22] Georgios B. Giannakis,et al. Consensus-Based Distributed Support Vector Machines , 2010, J. Mach. Learn. Res..
[23] Matt Welsh,et al. Simulating the power consumption of large-scale sensor network applications , 2004, SenSys '04.