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- W4362655364 abstract "Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning. This paper introduces a geometrically inspired kernel-based approach to mitigate the accuracy-loss issue in classification. In this approach, a representation of the affine hull of given data points is learned in Reproducing Kernel Hilbert Spaces (RKHS). This leads to a novel distance measure that hides privacy-sensitive information about individual data points and improves the privacy-utility tradeoff via significantly reducing the risk of membership inference attacks. The effectiveness of the approach is demonstrated through experiments on MNIST dataset, Freiburg groceries dataset, and a real biomedical dataset. It is verified that the approach remains computationally practical. The application of the approach to federated learning is considered and it is observed that the accuracy-loss due to data being distributed is either marginal or not significantly high." @default.
- W4362655364 created "2023-04-07" @default.
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- W4362655364 date "2023-04-03" @default.
- W4362655364 modified "2023-09-26" @default.
- W4362655364 title "On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach" @default.
- W4362655364 doi "https://doi.org/10.48550/arxiv.2304.01300" @default.
- W4362655364 hasPublicationYear "2023" @default.
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