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Privacy in Statistical Databases

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

  • Conference proceedings
  • © 2006

Overview

Editors:
  1. Josep Domingo-Ferrer

    1. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, UNESCO Chair in Data Privacy, Tarragona

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  2. Luisa Franconi

    1. Istat, Servizio Progettazione e Supporto Metodologico, nei Processi di Produzione Statistica, Roma, Italy

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

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 2006.

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Table of contents (31 papers)

  1. Front Matter

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  2. Methods for Tabular Protection

    1. A Method for Preserving Statistical Distributions Subject to Controlled Tabular Adjustment

      • Lawrence H. Cox, Jean G. Orelien, Babubhai V. Shah
      Pages 1-11

    2. Automatic Structure Detection in Constraints of Tabular Data

      • Jordi Castro, Daniel Baena
      Pages 12-24

    3. A New Approach to Round Tabular Data

      • Juan José Salazar González
      Pages 25-34

    4. Harmonizing Table Protection: Results of a Study

      • Sarah Giessing, Stefan Dittrich
      Pages 35-47

  3. Utility and Risk in Tabular Protection

    1. Effects of Rounding on the Quality and Confidentiality of Statistical Data

      • Lawrence H. Cox, Jay J. Kim
      Pages 48-56

    2. Disclosure Analysis for Two-Way Contingency Tables

      • Haibing Lu, Yingjiu Li, Xintao Wu
      Pages 57-67

    3. Statistical Disclosure Control Methods Through a Risk-Utility Framework

      • Natalie Shlomo, Caroline Young
      Pages 68-81

    4. A Generalized Negative Binomial Smoothing Model for Sample Disclosure Risk Estimation

      • Yosef Rinott, Natalie Shlomo
      Pages 82-93

    5. Entry Uniqueness in Margined Tables

      • Shmuel Onn
      Pages 94-101

  4. Methods for Microdata Protection

    1. Combinations of SDC Methods for Microdata Protection

      • Anna Oganian, Alan F. Karr
      Pages 102-113

    2. A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases

      • Ebaa Fayyoumi, B. John Oommen
      Pages 114-128

    3. Optimal Multivariate 2-Microaggregation for Microdata Protection: A 2-Approximation

      • Josep Domingo-Ferrer, Francesc Sebé
      Pages 129-138

    4. Using the Jackknife Method to Produce Safe Plots of Microdata

      • Jobst Heitzig
      Pages 139-151

    5. Combining Blanking and Noise Addition as a Data Disclosure Limitation Method

      • Anton Flossmann, Sandra Lechner
      Pages 152-163

    6. Why Swap When You Can Shuffle? A Comparison of the Proximity Swap and Data Shuffle for Numeric Data

      • Krish Muralidhar, Rathindra Sarathy, Ramesh Dandekar
      Pages 164-176

    7. Adjusting Survey Weights When Altering Identifying Design Variables Via Synthetic Data

      • Robin Mitra, Jerome P. Reiter
      Pages 177-188

  5. Utility and Risk in Microdata Protection

    1. Risk, Utility and PRAM

      • Peter-Paul de Wolf
      Pages 189-204

    2. Distance Based Re-identification for Time Series, Analysis of Distances

      • Jordi Nin, Vicenç Torra
      Pages 205-216

    3. Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk

      • Guy Lebanon, Monica Scannapieco, Mohamed R. Fouad, Elisa Bertino
      Pages 217-232
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  1. Privacy in Statistical Databases

Keywords

About this book

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.

Editors and Affiliations

  • Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, UNESCO Chair in Data Privacy, Tarragona

    Josep Domingo-Ferrer

  • Istat, Servizio Progettazione e Supporto Metodologico, nei Processi di Produzione Statistica, Roma, Italy

    Luisa Franconi

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