Non-interactive Private Decision Tree Evaluation
In this paper, we address the problem of privately evaluating a decision tree on private data. This scenario consists of a server holding a private decision tree model and a client interested in classifying its private attribute vector using the server’s
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Anoop Singhal Jaideep Vaidya (Eds.)
Data and Applications Security and Privacy XXXIV 34th Annual IFIP WG 11.3 Conference, DBSec 2020 Regensburg, Germany, June 25–26, 2020 Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this series at http://www.springer.com/series/7409
Anoop Singhal Jaideep Vaidya (Eds.) •
Data and Applications Security and Privacy XXXIV 34th Annual IFIP WG 11.3 Conference, DBSec 2020 Regensburg, Germany, June 25–26, 2020 Proceedings
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Editors Anoop Singhal National Institute of Standards and Technology Gaithersburg, MD, USA
Jaideep Vaidya Rutgers University Newark, NJ, USA
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-49668-5 ISBN 978-3-030-49669-2 (eBook) https://doi.org/10.1007/978-3-030-49669-2 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © IFIP International Federation for Information Processing 2020 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
This volume contains the papers selected for presentation at the 34th Annual IFIP WG11.3 Conference on Data and Applications Security and Privacy (DBSec 2020), that was supposed to be during June 25–26, 2020, in Regensburg. While the conference was held on the dates as scheduled, due t
