Privacy: Theory meets practice on the map

Ashwin Machanavajjhala, Daniel Kifer, John Abowd, Johannes Gehrke, Lars Vilhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

416 Scopus citations


In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics community. The target application for this work is a mapping program that shows the commuting patterns of the population of the United States. The source data for this application were collected by the U.S. Census Bureau, but due to privacy constraints, they cannot be used directly by the mapping program. Instead, we generate synthetic data that statistically mimic the original data while providing privacy guarantees. We use these synthetic data as a surrogate for the original data. We find that while some existing definitions of privacy are inapplicable to our target application, others are too conservative and render the synthetic data useless since they guard against privacy breaches that are very unlikely. Moreover, the data in our target application is sparse, and none of the existing solutions are tailored to anonymize sparse data. In this paper, we propose solutions to address the above issues.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Number of pages10
StatePublished - 2008
Event2008 IEEE 24th International Conference on Data Engineering, ICDE'08 - Cancun, Mexico
Duration: Apr 7 2008Apr 12 2008

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Other2008 IEEE 24th International Conference on Data Engineering, ICDE'08

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems


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