Skip to main content
SpringerLink
Log in
Book cover

Privacy in Statistical Databases

UNESCO Chair in Data Privacy International Conference, PSD 2008, Istanbul, Turkey, September 24-26, 2008, Proceedings

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Josep Domingo-Ferrer
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Yücel Saygın
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5262)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: PSD 2008.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 69.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (27 papers)

  1. Front Matter

    Download chapter PDF

  2. Tabular Data Protection

    1. Using a Mathematical Programming Modeling Language for Optimal CTA

      • Jordi Castro, Daniel Baena
      Pages 1-12

    2. A Data Quality and Data Confidentiality Assessment of Complementary Cell Suppression

      • Lawrence H. Cox
      Pages 13-23

    3. Pre-processing Optimisation Applied to the Classical Integer Programming Model for Statistical Disclosure Control

      • Martin Serpell, Alistair Clark, Jim Smith, Andrea Staggemeier
      Pages 24-36

    4. How to Make the τ-ARGUS Modular Method Applicable to Linked Tables

      • Peter-Paul de Wolf, Sarah Giessing
      Pages 37-49

    5. Bayesian Assessment of Rounding-Based Disclosure Control

      • Jon J. Forster, Roger C. Gill
      Pages 50-63

    6. Cell Bounds in Two-Way Contingency Tables Based on Conditional Frequencies

      • Byran Smucker, Aleksandra B. Slavković
      Pages 64-76

    7. Invariant Post-tabular Protection of Census Frequency Counts

      • Natalie Shlomo, Caroline Young
      Pages 77-89

  3. Microdata Protection: Methods and Case Studies

    1. A Practical Approach to Balancing Data Confidentiality and Research Needs: The NHIS Linked Mortality Files

      • Kimberly Lochner, Stephanie Bartee, Gloria Wheatcroft, Christine Cox
      Pages 90-99

    2. From t-Closeness to PRAM and Noise Addition Via Information Theory

      • David Rebollo-Monedero, Jordi Forné, Josep Domingo-Ferrer
      Pages 100-112

    3. Robustification of Microdata Masking Methods and the Comparison with Existing Methods

      • Matthias Templ, Bernhard Meindl
      Pages 113-126

    4. A Preliminary Investigation of the Impact of Gaussian Versus t-Copula for Data Perturbation

      • Mario Trottini, Krish Muralidhar, Rathindra Sarathy
      Pages 127-138

    5. Anonymisation of Panel Enterprise Microdata – Survey of a German Project

      • Maurice Brandt, Rainer Lenz, Martin Rosemann
      Pages 139-151

  4. Microdata Protection: Disclosure Risk Assessment

    1. Towards a More Realistic Disclosure Risk Assessment

      • Jordi Nin, Javier Herranz, Vicenç Torra
      Pages 152-165

    2. Assessing Disclosure Risk for Record Linkage

      • Chris Skinner
      Pages 166-176

    3. Robust Statistics Meets SDC: New Disclosure Risk Measures for Continuous Microdata Masking

      • Matthias Templ, Bernhard Meindl
      Pages 177-189

    4. Parallelizing Record Linkage for Disclosure Risk Assessment

      • Joan Guisado-Gámez, Arnau Prat-Pérez, Jordi Nin, Victor Muntés-Mulero, Josep Ll. Larriba-Pey
      Pages 190-202

    5. Extensions of the Re-identification Risk Measures Based on Log-Linear Models

      • Daniela Ichim
      Pages 203-212

    6. Use of Auxiliary Information in Risk Estimation

      • Loredana Di Consiglio, Silvia Polettini
      Pages 213-226

    7. Accounting for Intruder Uncertainty Due to Sampling When Estimating Identification Disclosure Risks in Partially Synthetic Data

      • Jörg Drechsler, Jerome P. Reiter
      Pages 227-238
Back to top

Other volumes

  1. Privacy in Statistical Databases

Keywords

About this book

Privacy in statistical databases is a discipline whose purpose is to provide solutions to the tension between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the various parties involved. Those parties are the respondents (the individuals and enterprises to which the database records refer), the data owners (those organizations spending money in data collection) and the users (the ones querying the database, who would like their queries to stay con?d- tial). Beyond law and ethics, there are also practical reasons for data collecting agencies to invest in respondent privacy: if individual respondents feel their p- vacyguaranteed,they arelikelyto providemoreaccurateresponses. Data owner privacy is primarily motivated by practical considerations: if an enterprise c- lects 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 increased user satisfaction, even if it may curtail the ability of the database owner to pro?le users. Thereareatleasttwotraditionsinstatisticaldatabaseprivacy,bothofwhich started in the 1970s: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originatesfrom computer science and database technology. In o?cial statistics, the basic c- cern is respondent privacy.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation