2008 PrivacyPreservingCoxRegressionf

Subject Headings: Privacy Preserving Task

Notes

Privacy-preserving data mining (PPDM) is an emergent research area that addresses the incorporation of privacy preserving concerns to data mining techniques. In this paper we propose a privacy-preserving (PP) Cox model for survival analysis, and consider a real clinical setting where the data is horizontally distributed among different institutions. The proposed model is based on linearly projecting the data to a lower dimensional space through an optimal mapping obtained by solving a linear programming problem. Our approach differs from the commonly used random projection approach since it instead finds a projection that is optimal at preserving the properties of the data that are important for the specific problem at hand. Since our proposed approach produces a sparse mapping, it also generates a PP mapping that not only projects the data to a lower dimensional space but it also depends on a smaller subset of the original features (it provides explicit feature selection). Real data from several European healthcare institutions are used to test our model for survival prediction of non-small-cell lung cancer patients. These results are also confirmed using publicly available benchmark datasets. Our experimental results show that we are able to achieve a near-optimal performance without directly sharing the data across different data sources. This model makes it possible to conduct large-scale multi-centric survival analysis without violating privacy-preserving requirements.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 PrivacyPreservingCoxRegressionf	Shipeng Yu Glenn Fung Romer Rosales Sriram Krishnan R. Bharat Rao Cary Dehing-Oberije Philippe Lambin			Privacy-preserving Cox Regression for Survival Analysis				10.1145/1401890.1402013