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Proceedings Paper

Privacy-preserving periodical publishing for medical information
Author(s): Hua Jin; Shi-guang Ju; Shan-cheng Liu
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Paper Abstract

Existing privacy-preserving publishing models can not meet the requirement of periodical publishing for medical information whether these models are static or dynamic. This paper presents a (k,l)-anonymity model with keeping individual association and a principle based on (Epsilon)-invariance group for subsequent periodical publishing, and then, the PKIA and PSIGI algorithms are designed for them. The proposed methods can reserve more individual association with privacy-preserving and have better publishing quality. Experiments confirm our theoretical results and its practicability.

Paper Details

Date Published: 19 July 2013
PDF: 6 pages
Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 88780V (19 July 2013); doi: 10.1117/12.2031757
Show Author Affiliations
Hua Jin, Jiangsu Univ. (China)
Shi-guang Ju, Jiangsu Univ. (China)
Shan-cheng Liu, Jiangsu Univ. (China)


Published in SPIE Proceedings Vol. 8878:
Fifth International Conference on Digital Image Processing (ICDIP 2013)
Yulin Wang; Xie Yi, Editor(s)

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