Secure Networked Inference with Unreliable Data Sources (eBook)

The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.

Aditya Vempaty received the B. Tech degree in Electrical Engineering from the Indian Institute of Technology, Kanpur, India, in 2011, and the PhD degree in Electrical Engineering and Computer Science from Syracuse University, in 2015. Since August 2015, he is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY. His research interests include human-machine inference networks, behavioral analytics, statistical signal processing, and network security. He received the All University Doctoral Prize 2016 by Syracuse University for superior achievement in completed dissertations.

Bhavya Kailkhura is a Postdoctoral Researcher at the Lawrence Livermore National Labs, Livermore, CA. His research interests include high dimensional data analytics, robust statistics & control, and machine learning. He was the runner-up for 'Best Student Paper Award' at IEEE Asilomar Conf. on Signals, Systems & Computers, 2014 and a SPS travel grant award recipient. He received the All University Doctoral Prize 2017 by Syracuse University for superior achievement in completed dissertations.

Pramod K. Varshney is a Distinguished Professor of Electrical Engineering and Computer Science and the Director of the Center for Advanced Systems and Engineering at Syracuse University, Syracuse, NY. His current research interests include distributed sensor networks and data fusion, detection and estimation theory, wireless communications, image processing, radar signal processing, and remote sensing. He is the author of Distributed Detection and Data Fusion (Springer-Verlag, 1997). He was a James Scholar, a Bronze Tablet Senior, and a Fellow while at the University of Illinois. He is a Member of Tau Beta Pi and received the 1981 ASEE Dow Outstanding Young Faculty Award. He was elected to the grade of Fellow of the IEEE in 1997 for his contributions in the area of distributed detection and data fusion. He was the Guest Editor of the Special Issue on Data Fusion of the PROCEEDINGS OF THE IEEE, January 1997. In 2000, he received the Third Millennium Medal from the IEEE and Chancellor's Citation for exceptional academic achievement at Syracuse University. He received the IEEE 2012 Judith A. Resnik Award, Doctor of Engineering Honoris Causa from Drexel University in 2014, and the ECE Distinguished Alumni Award from the University of Illinois in 2015. He is on the Editorial Boards of the Journal on Advances in Information Fusion and the IEEE Signal Processing Magazine. He was the President of the International Society of Information Fusion during 2001.

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The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.