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.
Deep neural language models are a critical part of many AI applications, but they also raise concerns about privacy and security. When these models are deployed on servers accessed through the internet, there are risks associated with transmitting user inputs over the network. Additionally, organizations that deploy these models may store user data with limited context, raising privacy concerns.
To address these issues, the authors propose a novel method that involves adapting and fine-tuning transformer-based language models on passkey-encrypted user-specific text. The original pre-trained language model is adapted quickly, without further pre-training, using irreversible transformations applied to the tokenizer and token embeddings. This allows the model to perform inference on encrypted inputs while protecting against reverse engineering of text from model parameters and intermediate outputs.
After adaptation, the models are fine-tuned on encrypted versions of existing training datasets. Experimental evaluation using adapted versions of well-known models like BERT and RoBERTa on benchmark English and multilingual datasets for text classification and sequence labeling shows that the encrypted models achieve performance similar to their original counterparts.
This approach has significant implications from a multi-disciplinary perspective. It combines expertise from the fields of natural language processing (NLP), cryptography, and computer security to address the privacy and security concerns associated with deep neural language models. The adaptation and fine-tuning process ensures that the models can maintain their performance while protecting user privacy and securing sensitive data.
Furthermore, this method can have broad applications across various AI-based systems, including chatbots, virtual assistants, and data analysis tools. By encrypting user inputs and preventing access to sensitive information, organizations can build trust with their users and comply with privacy regulations.
In conclusion, this novel approach of adapting and fine-tuning transformer-based language models on passkey-encrypted user-specific text has the potential to enhance the privacy, security, and performance of AI applications. By incorporating concepts from NLP, cryptography, and computer security, this method provides a comprehensive solution to address the risks associated with deep neural language models. Future research in this area could explore the applicability of this approach to other types of AI models and investigate additional encryption techniques to further strengthen the security of these models.