Scalable Differentially Private Bayesian Optimization

• URL: http://arxiv.org/abs/2502.06044v1
• Date: Sun, 09 Feb 2025 21:49:50 GMT
• Title: Scalable Differentially Private Bayesian Optimization
• Authors: Getoar Sopa, Juraj Marusic, Marco Avella-Medina, John P. Cunningham,
• Abstract summary: We develop a method to privately estimate potentially high-dimensional parameter spaces using Gradient Informative Bayesian Optimization. Our theoretical results prove that under suitable conditions, our method converges exponentially fast to a ball around the optimal parameter configuration. Regardless of whether the assumptions are satisfied, we show that our algorithm maintains privacy and empirically demonstrates superior performance to existing methods.
• Score: 17.28046301424826
• License:
• Abstract: In recent years, there has been much work on scaling Bayesian Optimization to high-dimensional problems, for example hyperparameter tuning in large neural network models. These scalable methods have been successful, finding high objective values much more quickly than traditional global Bayesian Optimization or random search-based methods. At the same time, these large neural network models often use sensitive data, but preservation of Differential Privacy has not scaled alongside these modern Bayesian Optimization procedures. Here we develop a method to privately estimate potentially high-dimensional parameter spaces using Gradient Informative Bayesian Optimization. Our theoretical results prove that under suitable conditions, our method converges exponentially fast to a ball around the optimal parameter configuration. Moreover, regardless of whether the assumptions are satisfied, we show that our algorithm maintains privacy and empirically demonstrates superior performance to existing methods in the high-dimensional hyperparameter setting.

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