Privacy-Preserving Statistical Inference for Stochastic Frontier Analysis Article Swipe

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Meiying Quan , Yunquan Song , Xinmin Wang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.3390/axioms14090667

We present the first differentially private framework for stochastic frontier analysis (SFA), addressing the challenge of non-convex objectives in privacy-preserving efficiency estimation. We construct a bounded parameter space to control gradient sensitivity and adapt the Frank–Wolfe algorithm with calibrated linear oracle noise to mitigate cumulative perturbation. Incorporating l1-regularization facilitates sparse and interpretable variable selection under strict (ϵ,δ)-differential privacy. Experiments demonstrate 15–35% MAE reduction under ϵ=0.1, along with strong scalability and estimation accuracy compared to prior DP methods for non-convex models.

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Frontier

Statistical Inference

Artificial Intelligence

Law

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Frontier Statistical inference Inference Computer science Econometrics Statistical analysis Fiducial inference Frequentist inference Data science Statistics Mathematics Artificial intelligence Bayesian inference Political science Bayesian probability Law

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/axioms14090667
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DOI: https://doi.org/10.3390/axioms14090667

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Title: Privacy-Preserving Statistical Inference for Stochastic Frontier Analysis

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Language: en

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Publication year: 2025

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Publication date: 2025-08-29

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Authors: Meiying Quan, Yunquan Song, Xinmin Wang

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Landing page: https://doi.org/10.3390/axioms14090667

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Concepts: Frontier, Statistical inference, Inference, Computer science, Econometrics, Statistical analysis, Fiducial inference, Frequentist inference, Data science, Statistics, Mathematics, Artificial intelligence, Bayesian inference, Political science, Bayesian probability, Law

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