Balancing privacy and data utilization is one of the most important challenges in modern society. These challenges are compounded by the privacy paradox. Protecting privacy while keeping data useful is a challenge. This study proposes a new approach that leverages the Large Language Model (LLM) to enhance privacy and security.

We explored how LLMs can be used to effectively anonymize personal information. This ensures the protection of personal information while maintaining the usefulness of the data. LLMs can also be used to analyze security-related texts to identify security threats and suggest countermeasures.

This research presents a new technological approach for more efficient privacy protection and enhanced security. By leveraging LLMs, we propose a way to leverage advances in natural language processing technology to enhance privacy and security at the same time.

Keywords

Privacy Security, Privacy Paradox, Large Language Model, Natural Language Processing and Data Protection,