Advertisement

Privacy in Statistical Databases

CENEX-SDC Project International Conference, PSD 2006, Rome, Italy, December 13-15, 2006. Proceedings

  • Josep Domingo-Ferrer
  • Luisa Franconi

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4302)

Table of contents

  1. Front Matter
  2. Methods for Tabular Protection

    1. Lawrence H. Cox, Jean G. Orelien, Babubhai V. Shah
      Pages 1-11
    2. Jordi Castro, Daniel Baena
      Pages 12-24
    3. Juan José Salazar González
      Pages 25-34
    4. Sarah Giessing, Stefan Dittrich
      Pages 35-47
  3. Utility and Risk in Tabular Protection

  4. Methods for Microdata Protection

  5. Utility and Risk in Microdata Protection

    1. Peter-Paul de Wolf
      Pages 189-204
    2. Guy Lebanon, Monica Scannapieco, Mohamed R. Fouad, Elisa Bertino
      Pages 217-232
    3. Vicenç Torra, John M. Abowd, Josep Domingo-Ferrer
      Pages 233-242
    4. Loredana Di Consiglio, Silvia Polettini
      Pages 243-256
  6. Protocols for Private Computation

    1. Alberto Maria Segre, Andrew Wildenberg, Veronica Vieland, Ying Zhang
      Pages 266-276
    2. Stephen E. Fienberg, William J. Fulp, Aleksandra B. Slavkovic, Tracey A. Wrobel
      Pages 277-290
  7. Case Studies

  8. Software

    1. Anco Hundepool
      Pages 334-346
    2. M. Templ
      Pages 347-359
    3. Milan Marković
      Pages 360-374
  9. Back Matter

About these proceedings

Introduction

Privacy in statistical databases is a discipline whose purpose is to provide - lutions to the con?ict between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the individuals and enterprises to which statistical data refer. - yond law and ethics, there are also practical reasons for statistical agencies and data collectors to invest in this topic: if individual and corporate respondents feel their privacyguaranteed,they arelikelyto providemoreaccurateresponses. There are at least two traditions in statistical database privacy: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originates from computer science and database technology.Bothstartedinthe1970s,butthe1980sandtheearly1990ssawlittle privacy activity on the computer science side. The Internet era has strengthened the interest of both statisticians and computer scientists in this area. Along with the traditional topics of tabular and microdata protection, some research lines have revived and/or appeared, such as privacy in queryable databases and protocols for private data computation.

Keywords

Estimator Excel Sage Time series anonymity bayesian model calculus data mining inference control information loss kernel density estimation learning microdata privacy proving

Editors and affiliations

  • Josep Domingo-Ferrer
    • 1
  • Luisa Franconi
    • 2
  1. 1.Department of Computer Engineering and MathematicsUniversitat Rovira i Virgili, UNESCO Chair in Data PrivacyTarragona
  2. 2.Istat, Servizio Progettazione e Supporto Metodologico, nei Processi di Produzione StatisticaRomaItaly

Bibliographic information

  • DOI https://doi.org/10.1007/11930242
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-49330-3
  • Online ISBN 978-3-540-49332-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
Industry Sectors
Automotive
Finance, Business & Banking
Electronics
IT & Software
Telecommunications
Consumer Packaged Goods
Aerospace