Security Measures of Textual Data

Abstract

As the volume and importance of textual data in data science continues to grow, combined with advancements in its techniques, it has created numerous opportunities for extracting valuable insights from textual information. However, privacy and the security of private data are issues that are brought up by the analysis of text data. This paper presents an in-depth analysis of security measures specifically designed for protecting textual data in data science applications. It explores various techniques and strategies to safeguard the confidentiality, integrity, and availability of textual data throughout its lifecycle. The objective is to provide data scientists and practitioners with a comprehensive understanding of the security challenges and best practices associated with textual data in the context of data science.