Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation

Abdelrahman Abouelenin; Mohamed Abdelrehim; Raffy Fahim; Amr Hendy; Mohamed Afify

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arXiv · 2505.05648

Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation

Abdelrahman Abouelenin·Mohamed Abdelrehim·Raffy Fahim·Amr Hendy·Mohamed Afify

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Abstract

In this paper we train a transformer using differential privacy (DP) for language modeling in SwiftKey. We run multiple experiments to balance the trade-off between the model size, run-time speed and accuracy. We show that we get small and consistent gains in the next-word-prediction and accuracy with graceful increase in memory and speed compared to the production GRU. This is obtained by scaling down a GPT2 architecture to fit the required size and a two stage training process that builds a seed model on general data and DP finetunes it on typing data. The transformer is integrated using ONNX offering both flexibility and efficiency.

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Abdelrahman Abouelenin, Mohamed Abdelrehim, Raffy Fahim, Amr Hendy, Mohamed Afify. 2025-05-08. Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation. https://arxiv.org/abs/2505.05648

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