  This paper addresses the privacy and security concerns associated with deep
neural language models, which serve as crucial components in various modern
AI-based applications. These models are often used after being pre-trained and
fine-tuned for specific tasks, with deployment on servers accessed through the
internet. However, this introduces two fundamental risks: (a) the transmission
of user inputs to the server via the network gives rise to interception
vulnerabilities, and (b) privacy concerns emerge as organizations that deploy
such models store user data with restricted context. To address this, we
propose a novel method to adapt and fine-tune transformer-based language models
on passkey-encrypted user-specific text. The original pre-trained language
model first undergoes a quick adaptation (without any further pre-training)
with a series of irreversible transformations applied to the tokenizer and
token embeddings. This enables the model to perform inference on encrypted
inputs while preventing reverse engineering of text from model parameters and
intermediate outputs. After adaptation, models are fine-tuned on encrypted
versions of existing training datasets. Experimental evaluation employing
adapted versions of renowned models (e.g., BERT, RoBERTa) across established
benchmark English and multilingual datasets for text classification and
sequence labeling shows that encrypted models achieve performance parity with
their original counterparts. This serves to safeguard performance, privacy, and
security cohesively.
