Engineering Dependable and Secure Machine Learning Systems Third Int
This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and
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ommunications in Computer and Information Science
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Engineering Dependable and Secure Machine Learning Systems Third International Workshop, EDSMLS 2020 New York City, NY, USA, February 7, 2020 Revised Selected Papers
Communications in Computer and Information Science Editorial Board Members Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Ashish Ghosh Indian Statistical Institute, Kolkata, India Raquel Oliveira Prates Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil Lizhu Zhou Tsinghua University, Beijing, China
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More information about this series at http://www.springer.com/series/7899
Onn Shehory Eitan Farchi Guy Barash (Eds.) •
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Engineering Dependable and Secure Machine Learning Systems Third International Workshop, EDSMLS 2020 New York City, NY, USA, February 7, 2020 Revised Selected Papers
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Editors Onn Shehory Bar-Ilan University Ramat Gan, Israel
Eitan Farchi IBM Haifa Research Lab Haifa, Israel
Guy Barash Bar-Ilan University Ramat Gan, Israel
ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-3-030-62143-8 ISBN 978-3-030-62144-5 (eBook) https://doi.org/10.1007/978-3-030-62144-5 © Springer Nature Switzerland AG 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, expressed 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
Contemporary software systems increasingly encompass machine learning (ML) components. In similarity to other software systems, ML-based software systems must meet dependability, security, and quality requirements. Standard notions of software quality and reliability such as deterministic functional correctness, black box testing, code coverage, and traditional software debugging may become irrelevant for ML s
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