Now showing items 1-3 of 3

    • A Bias-Variance-Privacy Trilemma for Statistical Estimation 

      Regehr, Matthew (University of Waterloo, 2023-08-28)
      The canonical algorithm for differentially private mean estimation is to first clip the samples to a bounded range and then add noise to their empirical mean. Clipping controls the sensitivity and, hence, the variance of ...
    • Differentially Private Learning with Noisy Labels 

      Mohapatra, Shubhankar (University of Waterloo, 2020-05-28)
      Supervised machine learning tasks require large labelled datasets. However, obtaining such datasets is a difficult task and often leads to noisy labels due to human errors or adversarial perturbation. Recent studies have ...
    • Differentially Private Online Aggregation 

      Sivasubramaniam, Harry (University of Waterloo, 2022-01-13)
      Database operations are often performed in batch mode, i.e. the analyst issuing the query must wait till the database has been processed in its entirety before getting feedback. Batch mode is inadequate for large databases ...

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