Paper Title:
A Novel Privacy Preserving Model for Datasets Re-Publication
  Abstract

Some medical records are often added and deleted in the practical applications. The leakage of privacy information caused by re-publishing datasets with multiple sensitive attributes becomes more likely than any other publication styles. In this paper, we first systematically characterize the inference attacks and set the hierarchy sensitive attribute rules. Then we propose a novel privacy preserving model based on k-anonymity for re-publication of multiple sensitive datasets and verify the novel approach that can eliminate inference channel and effectively protect privacy information in re-publication of datasets with multiple sensitive attributes by specific example. Finally, we present the anonymization algorithm to achieve privacy against inference attack.

  Info
Periodical
Advanced Materials Research (Volumes 108-111)
Edited by
Yanwen Wu
Pages
1433-1438
DOI
10.4028/www.scientific.net/AMR.108-111.1433
Citation
Y. Zhao, J. Wang, Q. S. Zhu, J. J. Le, "A Novel Privacy Preserving Model for Datasets Re-Publication", Advanced Materials Research, Vols. 108-111, pp. 1433-1438, 2010
Online since
May 2010
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Price
$32.00
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