FISIP: A distance and correlation preserving transformation for privacy preserving data mining

Jen Wei Huang, Jun Wei Su, Ming Syan Chen

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

5 Citations (Scopus)

Abstract

This paper devises a transformation scheme to protect data privacy in the case that data have to be sent to the third party for the analysis purpose. Most conventional transformation schemes suffer from two limits, i.e., the algorithm dependency and the information loss. In this work, we propose a novel privacy preserving transformation scheme without these two limitations. The transformation is referred to as FISIP. Explicitly, by preserving three basic properties, i.e., the first order sum, the second order sum and inner products, of the private data, mining algorithms which depend on these three properties can still be applied to public data. Specifically, any distance-based or correlation-based algorithm has the same performance on the transformed public data as on the original private data. Special perturbation can be added into FISIP transformations to increase the protection level. In the experimental results, FISIP attains data usefulness and data robustness at the same time. In summary, FISIP is able to provide a privacy preserving scheme that preserves the distance and the correlation of the private data after the transformation to the public data.

Original languageEnglish
Title of host publicationProceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011
Pages101-106
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event16th Annual Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011 - Chung-Li, Taiwan
Duration: 2011 Nov 112011 Nov 13

Publication series

NameProceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011

Other

Other16th Annual Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011
CountryTaiwan
CityChung-Li
Period11-11-1111-11-13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Fingerprint Dive into the research topics of 'FISIP: A distance and correlation preserving transformation for privacy preserving data mining'. Together they form a unique fingerprint.

Cite this