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

Jen Wei Huang, Jun Wei Su, Ming Syan Chen

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011
頁面101-106
頁數6
DOIs
出版狀態Published - 2011 十二月 1
事件16th Annual Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011 - Chung-Li, Taiwan
持續時間: 2011 十一月 112011 十一月 13

出版系列

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

Other

Other16th Annual Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011
國家Taiwan
城市Chung-Li
期間11-11-1111-11-13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

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  • 引用此

    Huang, J. W., Su, J. W., & Chen, M. S. (2011). FISIP: A distance and correlation preserving transformation for privacy preserving data mining. 於 Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011 (頁 101-106). [6120727] (Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011). https://doi.org/10.1109/TAAI.2011.25