Privacy-Preserving Clustering by Object Similarity-Based Representation and Dimensionality Reduction Transformation
- Stanley R. Oliveira
- Osmar R. Zaiane, University of Alberta (Database)
Preserving privacy of individuals when data are shared for clustering is a challenging problem. Data owners must not only meet privacy requirements but also guarantee valid clustering results. In this paper, we show that this dual goal can be achieved by transforming a database using two simple and effective data transformations: Object Similarity-Based Representation (OSBR) and Dimensionality Reduction-Based Transformation (DRBT). The former relies on the idea behind the similarity between objects, and the latter relies on the intuition behind random projection. The major features of our data transformations are: a) they are independent of distance-based clustering algorithms; b) they have a sound mathematical foundation; and c) they do not require CPU-intensive operations
Citation
S. Oliveira, O. Zaiane. "Privacy-Preserving Clustering by Object Similarity-Based Representation and Dimensionality Reduction Transformation". Workshop on Privacy and Security Aspects of Data Mining (PSDM), pp 21-30, November 2004.| Keywords: | |
| Category: | In Workshop |
BibTeX
@misc{Oliveira+Zaiane:PSDM04,
author = {Stanley R. Oliveira and Osmar R. Zaiane},
title = {Privacy-Preserving Clustering by Object Similarity-Based
Representation and Dimensionality Reduction Transformation},
Pages = {21-30},
booktitle = {Workshop on Privacy and Security Aspects of Data Mining (PSDM)},
year = 2004,
}Last Updated: February 04, 2020Submitted by Sabina P
