Privacy-preserving cox regression for survival analysis

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 an 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...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Shipeng Yu, Glenn Fung, Rómer Rosales, Sriram Krishnan, R. Bharat Rao, Cary Dehing-Oberije, Philippe Lambin
• Keywords: Data Mining, Data Mining Techniques, KDD 2008, Lower Dimensional Space, Privacy-preserving Data Mining