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Improved Generalization Guarantees in Restricted Data Models

Authors Elbert Du, Cynthia Dwork



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Author Details

Elbert Du
  • Department of Computer Science, Harvard University, Boston, MA, USA
Cynthia Dwork
  • Department of Computer Science, Harvard University, Boston, MA, USA

Acknowledgements

The authors are indebted to Guy Rothblum and Pragya Sur for many helpful conversations.

Cite AsGet BibTex

Elbert Du and Cynthia Dwork. Improved Generalization Guarantees in Restricted Data Models. In 3rd Symposium on Foundations of Responsible Computing (FORC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 218, pp. 6:1-6:12, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.FORC.2022.6

Abstract

Differential privacy is known to protect against threats to validity incurred due to adaptive, or exploratory, data analysis - even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the quantity of interest on the underlying population. The cost of this protection is the accuracy loss incurred by differential privacy. In this work, inspired by standard models in the genomics literature, we consider data models in which individuals are represented by a sequence of attributes with the property that where distant attributes are only weakly correlated. We show that, under this assumption, it is possible to "re-use" privacy budget on different portions of the data, significantly improving accuracy without increasing the risk of overfitting.

Subject Classification

ACM Subject Classification
  • Theory of computation → Machine learning theory
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
  • Differential Privacy
  • Adaptive Data Analysis
  • Transfer Theorem

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