document: 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 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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