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Area of Science:

  • Computer Science
  • Data Privacy
  • Machine Learning

Background:

  • Differential privacy is a leading framework for private data analysis.
  • It bounds how much a randomized function's output can change due to single record modifications.
  • The exponential mechanism is often used for weighted choices but can be computationally intensive with many outcomes.

Purpose of the Study:

  • To address the scalability limitations of the exponential mechanism in differential privacy.
  • To introduce a novel approach, the subsampled exponential mechanism, for efficient private data analysis.
  • To evaluate the privacy and accuracy guarantees of the subsampled exponential mechanism.

Main Methods:

  • Developed the subsampled exponential mechanism, which scores a sample of possible outcomes instead of all of them.
  • Proved that the subsampled exponential mechanism preserves differential privacy.
  • Established accuracy bounds comparable to the full exponential mechanism.
  • Applied the mechanism to a clustering problem for empirical evaluation.

Main Results:

  • The subsampled exponential mechanism maintains differential privacy guarantees.
  • It achieves accuracy bounds similar to the full exponential mechanism.
  • In a clustering application, it outperformed a previously published private algorithm.
  • It demonstrated comparable performance to the full exponential mechanism but with significantly improved scalability.

Conclusions:

  • The subsampled exponential mechanism is a scalable and effective method for private data analysis.
  • It provides a practical alternative to the full exponential mechanism when the outcome space is large.
  • This approach enhances the applicability of differential privacy in real-world scenarios with large datasets.