Concentration Bounds for High Sensitivity Functions Through Differential Privacy

    arXiv: Learning, Volume abs/1703.01970, 2017.

    Cited by: 3|Bibtex|Views2|Links
    EI

    Abstract:

    A new line of work demonstrates how differential privacy can be used as a mathematical tool for guaranteeing generalization in adaptive data analysis. Specifically, if a differentially private analysis is applied on a sample S of i.i.d. examples to select a low-sensitivity function f, then w.h.p. f(S) is close to its expectation, even tho...More

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