Secure Data Assimilation of Cloud Sensor Networks

[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.