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Privacy in Statistical Databases

UNESCO Chair in Data Privacy, International Conference, PSD 2010, Corfu, Greece, September 22-24, 2010. Proceedings

  • Josep Domingo-Ferrer
  • Emmanouil Magkos

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6344)

Table of contents

  1. Front Matter
  2. Tabular Data Protection

    1. Bing Liang, Kevin Chiew, Yingjiu Li, Yanjiang Yang
      Pages 1-16
    2. Jordi Castro, José A. González
      Pages 17-28
    3. Natalie Shlomo, Caroline Tudor, Paul Groom
      Pages 41-51
    4. Peter-Paul de Wolf, Anco Hundepool
      Pages 66-73
  3. Microdata Protection

    1. Spyros Sioutas, Emmanouil Magkos, Ioannis Karydis, Vassilios S. Verykios
      Pages 85-96
    2. Jordi Marés, Vicenç Torra
      Pages 97-106
    3. Anna Oganian
      Pages 107-117
    4. Arnau Erola, Jordi Castellà-Roca, Guillermo Navarro-Arribas, Vicenç Torra
      Pages 127-137
    5. Mark Elliot, Susan Lomax, Elaine Mackey, Kingsley Purdam
      Pages 138-147
  4. Synthetic Data

    1. Joseph W. Sakshaug, Trivellore E. Raghunathan
      Pages 162-173
  5. Differential Privacy

    1. Stephen E. Fienberg, Alessandro Rinaldo, Xiaolin Yang
      Pages 187-199
    2. Krish Muralidhar, Rathindra Sarathy
      Pages 200-209
    3. Rathindra Sarathy, Krish Muralidhar
      Pages 210-219
  6. On-Line Databases and Remote Access

    1. Philipp Bleninger, Jörg Drechsler, Gerd Ronning
      Pages 220-233
    2. Jason Lucero, Laura Zayatz
      Pages 234-248
    3. Wolf Heinrich Reuter, Jean-Marc Museux
      Pages 249-257
  7. Privacy-Preserving Protocols

    1. Josep Domingo-Ferrer
      Pages 258-268
    2. Rob Hall, Stephen E. Fienberg
      Pages 269-283
  8. Legal Issues

  9. Back Matter

About these proceedings

Introduction

Privacy in statistical databases is a discipline whose purpose is to provide so- tionstothetensionbetweenthesocial,political,economicandcorporatedemand for accurate information, and the legal and ethical obligation to protect the p- vacy of the various parties involved. Those parties are the respondents (the individuals and enterprises to which the database records refer), the data o- ers (those organizations spending money in data collection) and the users (the ones querying the database or the search engine, who would like their queries to stay con?dential). Beyond law and ethics, there are also practical reasons for data-collecting agencies and corporations to invest in respondent privacy: if individual respondents feel their privacy guaranteed, they are likely to provide moreaccurateresponses. Data ownerprivacyis primarilymotivatedbypractical considerations: if an enterprise collects data at its own expense, it may wish to minimize leakage of those data to other enterprises (even to those with whom joint data exploitation is planned). Finally, user privacy results in increaseduser satisfaction, even if it may curtail the ability of the database owner to pro?le users. Thereareatleasttwotraditionsinstatisticaldatabaseprivacy,bothofwhich started in the 1970s: the ?rst one stems from o?cial statistics, where the dis- pline is also known as statistical disclosure control (SDC), and the second one originates from computer science and database technology. In o?cial statistics, the basic concern is respondent privacy. In computer science, the initial mo- vation was also respondent privacy but, from 2000 onwards, growing attention has been devoted to owner privacy (privacy-preserving data mining) and user privacy (private informationretrieval).

Keywords

algorithm analysis data mining database microdata protection privacy tabular data protection

Editors and affiliations

  • Josep Domingo-Ferrer
    • 1
  • Emmanouil Magkos
    • 2
  1. 1.Department of Computer Engineering and MathematicsUniversitat Rovira i Virgili, UNESCO Chair in Data PrivacyTarragona
  2. 2.Department of InformaticsIonian UniversityKerkyraGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-15838-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-15837-7
  • Online ISBN 978-3-642-15838-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site