Investigation on Privacy-Preserving Techniques for Personal Data

Abstract

Privacy protection technology has become a crucial part of almost every existing cross-data analysis application. The privacy-preserving technique allows sharing sensitive personal information and preserves the users' privacy. This new trend influences data collection results by improving the analytical accuracy, increasing the number of participants, and better understand the participants' environments. Herein, collecting these personal data is significant to many advantageous applications such as health monitoring. Nevertheless, these applications encounter real privacy threats and concerns about handling personal information. This paper aims to determine privacy-preserving personal data mining technologies and analyze these technologies' advantages and shortcomings. Our purpose is to provide an in-depth understanding of personal data privacy and highlight important viewpoints, existing challenges, and future research directions.