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Differentially Private ADMM Algorithms for Machine Learning
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DOI：10.1109/TIFS.2021.3113768.png)
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Abstract
En 中文
In this paper, we study efficient differentially private alternating direction methods of multipliers (ADMM) via gradient perturbation for many centralized machine learning problems. For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with the performance guarantee of (epsilon, delta)-differential privacy ((epsilon, delta)-DP). From the viewpoint of theoretical analysis, we use the Gaussian mechanism and the conversion relationship between Renyi Differential Privacy (RDP) and DP to perform a comprehensive privacy analysis for our algorithm. Then we establish a new criterion to prove the convergence of the proposed algorithms including DP-ADMM. We also give the utility analysis of our DP-ADMM. Moreover, we propose a new accelerated DP-ADMM (DP-AccADMM) algorithm with the Nesterov's acceleration technique. Finally, we conduct numerical experiments on many real-world datasets to show the privacy-utility tradeoff of the two proposed algorithms, and all the comparative analysis shows that DP-AccADMM converges faster and has a better utility than DP-ADMM, when the privacy budget epsilon is larger than a threshold.
Keywords:
Privacy
Perturbation methods
Differential privacy
Convergence
Approximation algorithms
Convex functions
Machine learning algorithms
Differentially private
alternating direction method of multipliers (ADMM)
gradient perturbation
momentum acceleration
Gaussian mechanism
Journal
IF:
8
Papers:
5.2K
Citations:
2.3W