Privacy-Preserving Collaborative Prediction using Random Forests
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We study the problem of privacy-preserving machine learning (PPML) for ensemble methods, focusing our effort on random forests. In collaborative analysis, PPML attempts to solve the conflict between the need for data sharing and privacy. This is especially important in privacy sensitive applications such as learning predictive models for clinical decision support from EHR data from different clinics, where each clinic has a responsibility for its patients' privacy. We propose a new approach for ensemble methods: each entity learns a model, from its own data, and then when a client asks the prediction for a new private instance, the answers from all the locally trained models are used to compute the prediction in such a way that no extra information is revealed. We implement this approach for random forests and we demonstrate its high efficiency and potential accuracy benefit via experiments on real-world datasets, including actual EHR data.
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Scholars@Duke
David Page
David Page, PhD, serves as chair of the Department of Biostatistics and Bioinformatics and professor of biostatistics and bioinformatics and computer science at Duke University. He joined Duke in 2019. Dr. Page works on algorithms for data mining and machine learning and their applications to biomedical data. His research focuses on machine learning methods for complex multi-relational data, such as electronic health records, high throughput genetic and molecular data, and irregular temporal data, and methods that find causal relationships and produce human-interpretable output.
Dr. Page co-leads the Duke Discovery AI initiative which unites computational scientists, biologists, and engineers across the university to advance integration of artificial intelligence with biological research at the molecular, cellular, and organism scales. Together they train the next generation of scientists in biological systems and computational methods.
During his 20 years at the University of Wisconsin-Madison, Dr. Page taught courses titled Advanced AI, Machine Learning, Bioinformatics, and Health Informatics, in addition to special topics courses on Statistical Relational Learning and Learning Biological Networks. Dr. Page was a Kellett and Vilas Distinguished Achievement Professor and was Director of the Informatics Core of the Carbone Cancer Center. He served on scientific advisory and leadership committees for the Observational Medical Outcomes Partnership (OMOP), the International Warfarin Pharmacogenetics Consortium (IWPC), the Wisconsin Genomics Initiative, and UW-Madison's Institute for Clinical & Translational Science. Dr. Page received his PhD in computer science from the University of Illinois at Urbana-Champaign, where his dissertation focused on theoretical aspects of machine learning. He first became involved in biomedical applications of machine learning during his post-doc working with Dr. Stephen Muggleton at Oxford University.
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