Architecture Matters: Investigating the Influence of Differential Privacy on Neural Network DesignDownload PDF

Sep 16, 2021PRIML 2021 PosterReaders: Everyone
  • Keywords: neural networks, neural network architecture, differential privacy, privacy-preserving machine learning
  • TL;DR: We explore the relationship between neural network architectures and model accuracy under differential privacy constraints.
  • Abstract: We explore the relationship between neural network architectures and model accuracy under differential privacy constraints. Our findings show that architectures that perform well without differential privacy, do not necessarily do so with differential privacy. This shows that extant knowledge on neural network architecture design cannot be seamlessly translated into the differential privacy context. Moreover, as neural architecture search consumes privacy budget, future research is required to better understand the relationship between neural network architectures and model accuracy to enable better architecture design choices under differential privacy constraints.
  • Paper Under Submission: The paper is NOT under submission at NeurIPS
1 Reply

Loading