Equivalence Testing Under Privacy Constraints

2025-10-17 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

Savita Pareek et al. introduce DP-TOST, a unified framework for differentially private equivalence testing of means and proportions, addressing critical privacy concerns in sensitive domains like healthcare. Standard equivalence testing, which assesses if an effect lies within a pre-specified tolerance region, can inadvertently disclose sensitive information. DP-TOST leverages simulation-based calibration to handle the analytically intractable finite-sample distribution, ensuring strong privacy guarantees through Differential Privacy (DP) by adding calibrated random noise. Numerical simulations and real-world applications, including an analysis of the ACTG175 HIV clinical trial data, demonstrate that DP-TOST maintains type-I error control at nominal levels and achieves power comparable to its non-private counterpart as the privacy budget (epsilon) and/or sample size increases. The framework is implemented in the cTOST R package.

Key takeaway

For AI Scientists and Data Scientists working with sensitive biomedical or healthcare data, DP-TOST provides a robust method to conduct equivalence testing while adhering to strict privacy regulations like HIPAA and GDPR. You should consider integrating DP-TOST, especially with privacy budgets of epsilon ≈ 0.5 for proportions and epsilon ≈ 1 for means with sample sizes around 500, to balance strong privacy guarantees with statistical power, ensuring valid and ethical data analysis.

Key insights

DP-TOST offers a simulation-based framework for privacy-preserving equivalence testing of means and proportions.

Principles

• Differential Privacy ensures individual data protection.
• Equivalence testing assumes non-equivalence as the null.
• Simulation-based calibration addresses analytical intractability.

Method

DP-TOST uses a simulation-based moment-matching approach to construct differentially private confidence intervals. It adds calibrated noise to statistics (means/proportions) and then simulates to find parameters that best match observed privatized estimators.

In practice

• Use DP-TOST for comparing treatment efficacy across hospitals.
• Apply DP-TOST to ensure consistency in diagnostic lab calibrations.
• Utilize the cTOST R package for implementation.

Topics

• Equivalence Testing
• Differential Privacy
• DP-TOST Framework
• Simulation-based Inference
• Privacy Budget

Code references

• stephaneguerrier/cTOST
• mjuraska/speff2trial

Best for: AI Scientist, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.