PriViT: Vision Transformers for Private Inference

The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for private inference using secure multi-party computation (MPC) protocols, due to the large number of non-polynomial operations (self-attention, feed-forward rectifiers, layer normalization). We develop PriViT, a gradient-based algorithm to selectively Taylorize nonlinearities in ViTs while maintaining their prediction accuracy. Our algorithm is conceptually very simple, easy to implement, and achieves improved performance over existing MPC-friendly transformer architectures in terms of the latency-accuracy Pareto frontier.

Authors

• Naren Dhyani
• Jianqiao Cambridge Mo
• Patrick Yubeaton
• Minsu Cho
• Ameya Joshi
• Siddharth Garg
• Brandon Reagen
• Chinmay Hegde

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