Web Applications: k-Indistinguishable Traffic Padding
In this chapter, we present a formal Privacy-Preserving Traffic Padding (PPTP) model encompassing the privacy requirements, padding costs, and padding methods to prevent side-channel leaks due to unique patterns in packet sizes and directions of the encry
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Wen Ming Liu Lingyu Wang
Preserving Privacy Against Side-Channel Leaks From Data Publishing to Web Applications
Advances in Information Security Volume 68
Series Editor Sushil Jajodia, George Mason University, Fairfax, VA, USA
More information about this series at http://www.springer.com/series/5576
Wen Ming Liu • Lingyu Wang
Preserving Privacy Against Side-Channel Leaks From Data Publishing to Web Applications
123
Wen Ming Liu Concordia Institute for Information Systems Engineering Concordia University Montreal, QC, Canada
Lingyu Wang Concordia Institute for Information Systems Engineering Concordia University Montreal, QC, Canada
ISSN 1568-2633 Advances in Information Security ISBN 978-3-319-42642-6 ISBN 978-3-319-42644-0 (eBook) DOI 10.1007/978-3-319-42644-0 Library of Congress Control Number: 2016948836 © Springer International Publishing Switzerland 2016 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. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
To my wife, Bai Rong. – Wen Ming Liu
To my wife Quan, with love. – Lingyu Wang
Preface1
With rapid advancements in information technology, today’s organizations routinely collect, store, analyze, and redistribute vast amounts of data about individuals, such as user account information and online activities. In addition, the next generation of smart systems (e.g., smart grids and smart medical devices) will enable organizations to collect personal data about every aspect of our daily life, from realtime power consumption to medical conditions. Although collecting data may be essential for organizations to conduct their business, indiscriminate collection, retention, and dissemination of personal data represents a serious intrusion to the privacy of individuals. As a fundamental right of all individuals, privacy protection means organizations should only collect and retain sensitive personal information f
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