A Study of Privacy-preserving Language Modeling Approaches

TL;DR

This paper provides a comprehensive overview of privacy-preserving techniques in language modeling, analyzing their strengths, limitations, and implications for protecting sensitive data in AI applications.

Contribution

It offers an in-depth survey of existing privacy-preserving methods in language modeling, highlighting gaps and suggesting future research directions.

Findings

Identifies key strengths of current privacy-preserving approaches.

Highlights limitations and challenges in existing methods.

Provides insights for future research in privacy-preserving language models.

Abstract

Recent developments in language modeling have increased their use in various applications and domains. Language models, often trained on sensitive data, can memorize and disclose this information during privacy attacks, raising concerns about protecting individuals' privacy rights. Preserving privacy in language models has become a crucial area of research, as privacy is one of the fundamental human rights. Despite its significance, understanding of how much privacy risk these language models possess and how it can be mitigated is still limited. This research addresses this by providing a comprehensive study of the privacy-preserving language modeling approaches. This study gives an in-depth overview of these approaches, highlights their strengths, and investigates their limitations. The outcomes of this study contribute to the ongoing research on privacy-preserving language modeling, providing valuable insights and outlining future research directions.