Database Anonymization. David Sánchez
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Database Anonymization
Privacy Models, Data Utility, and Microaggregation-based Inter-model Connections
Synthesis Lectures on Information Security, Privacy, & Trust
Editor
Elisa Bertino, Purdue University
Ravi Sandhu, University of Texas at San Antonio
The Synthesis Lectures Series on Information Security, Privacy, and Trust publishes 50- to 100-page publications on topics pertaining to all aspects of the theory and practice of Information Security, Privacy, and Trust. The scope largely follows the purview of premier computer security research journals such as ACM Transactions on Information and System Security, IEEE Transactions on Dependable and Secure Computing and Journal of Cryptology, and premier research conferences, such as ACM CCS, ACM SACMAT, ACM AsiaCCS, ACM CODASPY, IEEE Security and Privacy, IEEE Computer Security Foundations, ACSAC, ESORICS, Crypto, EuroCrypt and AsiaCrypt. In addition to the research topics typically covered in such journals and conferences, the series also solicits lectures on legal, policy, social, business, and economic issues addressed to a technical audience of scientists and engineers. Lectures on significant industry developments by leading practitioners are also solicited.
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Copyright © 2016 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.
Database Anonymization:
Privacy Models, Data Utility, and Microaggregation-based Inter-model Connections
Josep Domingo-Ferrer, David Sánchez, and Jordi Soria-Comas
www.morganclaypool.com
ISBN: 9781627058438 paperback
ISBN: 9781627058445 ebook
DOI 10.2200/S00690ED1V01Y201512SPT015
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON INFORMATION SECURITY, PRIVACY, & TRUST
Lecture #15
Series Editors: | Elisa Bertino, Purdue University |
Ravi Sandhu, University of Texas at San Antonio |
Series ISSN
Print 1945-9742 Electronic 1945-9750
Database Anonymization
Privacy Models, Data Utility, and Microaggregation-based Inter-model Connections
Josep Domingo-Ferrer, David Sánchez, and Jordi Soria-Comas
Universitat Rovira i Virgili, Tarragona, Catalonia
SYNTHESIS LECTURES ON INFORMATION SECURITY, PRIVACY, & TRUST #15
ABSTRACT
The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guarantees they offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer.
KEYWORDS
data releases, privacy protection, anonymization, privacy models, statistical disclosure limitation, statistical disclosure control, microaggregation