Navigating Data Privacy and Analytics: The Role of Large Language Models in Masking conversational data in data platforms

Abstract

In the rapidly evolving landscape of data analytics, safeguarding conversational data privacy presents a pivotal challenge, especially with third-party enterprises commonly offering analytic services. This paper delves into the innovative application of Large Language Models (LLMs) for real-time masking of sensitive information in conversational data. The focus is on balancing privacy protection and data utility for analytics within a multi-stakeholder framework. The significance of data privacy is underscored across sectors, with specific attention to challenges in industries like healthcare, particularly when analytics involve external entities. A comprehensive literature review reveals limitations in existing data masking techniques and explores the role of LLMs in diverse contexts, extending beyond direct healthcare applications.The proposed methodology utilizes LLMs for real-time entity recognition and replacement, effectively masking sensitive information while adhering to privacy regulations. This approach is particularly pertinent for third-party analytics providers dealing with conversational data from various sources. Hypothetical case studies, including healthcare scenarios, showcase the practical application and efficacy of the method in real-world settings with external data analytics providers. The dual assessment evaluates the method’s efficiency in preserving privacy and maintaining data utility for analytical purposes. Experimental results using synthetically generated healthcare conversational data sets further illustrate the effectiveness of the approach in typical third-party analytics service scenarios.The discussion highlights broader implications, addressing challenges and limitations [1] across industries, and emphasizes ethical considerations in handling sensitive data by external entities. In conclusion, the paper summarizes the significant strides achievable with LLMs for data masking, with implications for diverse sectors and analytics providers. Future research directions, especially fine-tuning LLMs for enhanced performance in varied analytic scenarios, are suggested. This study sets the stage for a harmonious coexistence of customer data protection and utility in the intricate ecosystem of data analytics services, facilitated by the advanced capabilities of LLM technology.