(Near) Dimension Independent Risk Bounds for Differentially Private Learning

@inproceedings{Jain2014NearDI,
  title={(Near) Dimension Independent Risk Bounds for Differentially Private Learning},
  author={Prateek Jain and Abhradeep Thakurta},
  booktitle={ICML},
  year={2014}
}
In this paper, we study the problem of differentially private risk minimization where the goal is to provide differentially private algorithms that have small excess risk. In particular we address the following open problem: Is it possible to design computationally efficient differentially private risk minimizers with excess risk bounds that do not explicitly depend on dimensionality (p) and do not require structural assumptions like restricted strong convexity? In this paper, we answer the… CONTINUE READING
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