Privacy-preserving logistic regression

@inproceedings{Chaudhuri2008PrivacypreservingLR,
  title={Privacy-preserving logistic regression},
  author={Kamalika Chaudhuri and Claire Monteleoni},
  booktitle={NIPS},
  year={2008}
}
This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. We focus on privacy-preserving logistic regression. First we apply an idea of Dwork et al. [6] to design a privacy-preserving logistic regression algorithm. This involves bounding the sensitivity of regularized logistic regression, and perturbing the learned classifier with noise proportional to the sensitivity. We then provide a privacy-preserving… CONTINUE READING
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