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Models and methods for privacy-preserving data publishing and analysis: invited tutorial

Published: 13 June 2005 Publication History

Abstract

The digitization of our daily lives has led to an explosion in the collection of digital data by governments, corporations, and individuals. Protection of confidentiality of this data is of utmost importance. However, knowledge of statistical properties of this private data can have significant societal benefit, for example, in decisions about the allocation of public funds based on Census data, or in the analysis of medical data from different hospitals to understand the interaction of drugs.This tutorial will survey recent research that builds bridges between the two seemingly conflicting goals of sharing data while preserving data privacy and confidentiality. The tutorial will cover definitions of privacy and disclosure, and associated methods how to enforce them.More information, including a list of references to related work can be found at the following website: http://www.cs.cornell.edu/database/privacy.

Cited By

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  • (2025)Privacy-Preserving Data PublishingEncyclopedia of Cryptography, Security and Privacy10.1007/978-3-030-71522-9_1554(1937-1942)Online publication date: 8-Jan-2025
  • (2021)Overview of Privacy Protection Data Release Anonymity Technology2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)10.1109/BigDataSecurityHPSCIDS52275.2021.00037(151-156)Online publication date: May-2021
  • (2019)Dyadic product and crow lion algorithm based coefficient generation for privacy protection on cloudCluster Computing10.1007/s10586-017-1589-622:1(1277-1288)Online publication date: 1-Jan-2019
  • Show More Cited By

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cover image ACM Conferences
PODS '05: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
June 2005
388 pages
ISBN:1595930620
DOI:10.1145/1065167
  • General Chair:
  • Georg Gottlob,
  • Program Chair:
  • Foto Afrati
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2005

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Overall Acceptance Rate 642 of 2,707 submissions, 24%

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Cited By

View all
  • (2025)Privacy-Preserving Data PublishingEncyclopedia of Cryptography, Security and Privacy10.1007/978-3-030-71522-9_1554(1937-1942)Online publication date: 8-Jan-2025
  • (2021)Overview of Privacy Protection Data Release Anonymity Technology2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)10.1109/BigDataSecurityHPSCIDS52275.2021.00037(151-156)Online publication date: May-2021
  • (2019)Dyadic product and crow lion algorithm based coefficient generation for privacy protection on cloudCluster Computing10.1007/s10586-017-1589-622:1(1277-1288)Online publication date: 1-Jan-2019
  • (2018)A Survey on Privacy Preserving Dynamic Data PublishingInternational Journal of Organizational and Collective Intelligence10.4018/IJOCI.20181001018:4(1-20)Online publication date: 1-Oct-2018
  • (2008)On disclosure risk analysis of anonymized itemsets in the presence of prior knowledgeACM Transactions on Knowledge Discovery from Data10.1145/1409620.14096232:3(1-44)Online publication date: 27-Oct-2008

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