Secure Networked Inference with Unreliable Data Sources
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 s
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e Networked Inference with Unreliable Data Sources
Secure Networked Inference with Unreliable Data Sources
Aditya Vempaty Bhavya Kailkhura Pramod K. Varshney •
Secure Networked Inference with Unreliable Data Sources
123
Aditya Vempaty IBM Research—Thomas J. Watson Research Yorktown Heights, NY, USA
Pramod K. Varshney Department of Electrical Engineering and Computer Science Syracuse University Syracuse, NY, USA
Bhavya Kailkhura Department of Computing Applications and Research Lawrence Livermore National Laboratory Livermore, CA, USA
ISBN 978-981-13-2311-9 ISBN 978-981-13-2312-6 https://doi.org/10.1007/978-981-13-2312-6
(eBook)
Library of Congress Control Number: 2018952900 © Springer Nature Singapore Pte Ltd. 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
To our families My parents Anil and Radha My wife Swetha Aditya Vempaty My parents Umesh and Anu My brother Lakshya Bhavya Kailkhura My wife Anju Pramod K. Varshney
Preface
With an explosion in the number of connected devices and the emergence of big and dirty data era, new distributed learning solutions are needed to tackle the problem of inference with corrupted data. The aim of this book is to present theory and algorithms for secure networked inference in the presence of unreliable data sources. More specifically, we present fundamental limits of networked inference in the presence of Byzantine data (malicious data sources) and discuss robust mitigation strategies to ensure reliable performance for several practical network architectures. In particular, the inference (or learning) process can be detection, estimation, or classification, and the network architecture of the system can be paralle
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