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Privacy Preserving Principal Component Analysis Clustering for Distributed Heterogeneous Gene Expression Datasets

Privacy Preserving Principal Component Analysis Clustering for Distributed Heterogeneous Gene Expression Datasets

Xin Li
Copyright: © 2011 |Volume: 2 |Issue: 4 |Pages: 34
ISSN: 1947-3133|EISSN: 1947-3141|EISBN13: 9781613506066|DOI: 10.4018/jcmam.2011100102
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MLA

Li, Xin. "Privacy Preserving Principal Component Analysis Clustering for Distributed Heterogeneous Gene Expression Datasets." IJCMAM vol.2, no.4 2011: pp.23-56. http://doi.org/10.4018/jcmam.2011100102

APA

Li, X. (2011). Privacy Preserving Principal Component Analysis Clustering for Distributed Heterogeneous Gene Expression Datasets. International Journal of Computational Models and Algorithms in Medicine (IJCMAM), 2(4), 23-56. http://doi.org/10.4018/jcmam.2011100102

Chicago

Li, Xin. "Privacy Preserving Principal Component Analysis Clustering for Distributed Heterogeneous Gene Expression Datasets," International Journal of Computational Models and Algorithms in Medicine (IJCMAM) 2, no.4: 23-56. http://doi.org/10.4018/jcmam.2011100102

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Abstract

In this paper, we present approaches to perform principal component analysis (PCA) clustering for distributed heterogeneous genomic datasets with privacy protection. The approaches allow data providers to collaborate together to identify gene profiles from a global viewpoint, and at the same time, protect the sensitive genomic data from possible privacy leaks. We then further develop a framework for privacy preserving PCA-based gene clustering, which includes two types of participants: data providers and a trusted central site (TCS). Two different methodologies are employed: Collective PCA (C-PCA) and Repeating PCA (R-PCA). The C-PCA requires local sites to transmit a sample of original data to the TCS and can be applied to any heterogeneous datasets. The R-PCA approach requires all local sites have the same or similar number of columns, but releases no original data. Experiments on five independent genomic datasets show that both C-PCA and R-PCA approaches maintain very good accuracy compared with the centralized scenario.

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