When Relaxations Go Bad: "Differentially-Private" Machine Learning

We have posted a paper by Bargav Jayaraman and myself on When Relaxations Go Bad: “Differentially-Private” Machine Learning (code available at https://github.com/bargavj/EvaluatingDPML). Differential privacy is becoming a standard notion for performing privacy-preserving machine learning over sensitive data. It provides formal guarantees, in terms of the privacy budget, ε, on how much information about individual training records is leaked by the model. While the privacy budget is directly correlated to the privacy leakage, the calibration of the privacy budget is not well understood.


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