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Updated: Oct 22, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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ABCDP: Approximate Bayesian Computation with Differential Privacy.

Mijung Park1, Margarita Vinaroz2,3, Wittawat Jitkrittum4

  • 1Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

Entropy (Basel, Switzerland)
|August 27, 2021
PubMed
Summary
This summary is machine-generated.

We introduce ABCDP, a novel framework for differentially private (DP) approximate Bayesian computation (ABC). It uses sparse vector techniques to reduce privacy loss, enabling accurate posterior sampling with enhanced privacy.

Keywords:
approximate Bayesian computation (ABC)differential privacy (DP)sparse vector technique (SVT)

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Area of Science:

  • Computational Statistics
  • Statistical Inference
  • Data Privacy

Background:

  • Approximate Bayesian computation (ABC) is a powerful tool for statistical inference, especially with complex models.
  • Differential privacy (DP) offers rigorous guarantees for data privacy but can be challenging to integrate with existing statistical methods.
  • Existing DP methods often incur significant privacy costs with repeated computations.

Purpose of the Study:

  • To develop a novel framework, ABCDP, that integrates differential privacy into approximate Bayesian computation.
  • To enable the generation of differentially private approximate posterior samples.
  • To minimize the privacy cost associated with ABC while maintaining sample accuracy.

Main Methods:

  • Developed a new ABC framework (ABCDP) leveraging the sparse vector technique (SVT) for differential privacy.
  • SVT incurs privacy cost only when a specific condition (distance threshold) is met, reducing cumulative privacy loss.
  • Theoretically analyzed the trade-off between added noise for privacy and posterior sample accuracy.

Main Results:

  • ABCDP successfully generates differentially private approximate posterior samples.
  • The SVT approach significantly reduces privacy loss in ABC when the condition is sparsely met.
  • Empirical evaluation on various data simulators demonstrated the framework's efficacy.

Conclusions:

  • ABCDP provides an effective and minimally modified approach to achieve differential privacy in ABC.
  • The framework offers a high level of privacy for posterior samples without substantial loss of accuracy.
  • This work paves the way for privacy-preserving statistical inference in complex models.