Recovering from Privacy-Preserving Masking with Large Language Models
Authors: name: Arpita Vats, name: Zhe Liu, name: Peng Su, name: Debjyoti Paul, name: Yingyi Ma, name: Yutong Pang, name: Zeeshan Ahmed, name: Ozlem Kalinli

Model adaptation is crucial to handle the discrepancy between proxy training
data and actual users data received. To effectively perform adaptation, textual
data of users is typically stored on servers or their local devices, where
downstream natural language processing (NLP) models can be directly trained
using such in-domain data. However, this might raise privacy and security
concerns due to the extra risks of exposing user information to adversaries.
Replacing identifying information in textual data with a generic marker has
been recently explored. In this work, we leverage large language models (LLMs)
to suggest substitutes of masked tokens and have their effectiveness evaluated
on downstream language modeling tasks. Specifically, we propose multiple
pre-trained and fine-tuned LLM-based approaches and perform empirical studies
on various datasets for the comparison of these methods. Experimental results
show that models trained on the obfuscation corpora are able to achieve
comparable performance with the ones trained on the original data without
privacy-preserving token masking.