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- W2906083151 abstract "This thesis describes the design and implementationof a system which allows users to generate machine learning modelswith their own data while preserving privacy. We approach theproblem in two steps. First, we present a framework with which auser can collate personal data from a variety of sources in orderto generate machine learning models for problems of the user'schoosing. Second, we describe AnonML, a system which allows a groupof users to share data privately in order to build models forclassification. We analyze AnonML under differential privacy andtest its performance on real-world datasets. In tandem, these twosystems will help democratize machine learning, allowing people tomake the most of their own data without relying on trusted thirdparties." @default.
- W2906083151 created "2019-01-01" @default.
- W2906083151 creator A5089693938 @default.
- W2906083151 date "2017-01-01" @default.
- W2906083151 modified "2023-09-24" @default.
- W2906083151 title "A system for privacy-preserving machine learning on personal data" @default.
- W2906083151 hasPublicationYear "2017" @default.
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