Analyzing effects of privacy models on smart healthcare data

Abstract

Privacy concerns in smart healthcare arise due to the collection, storage, and sharing of personal health data through various interconnected devices and networks, coupled with the widespread use of data-gathering tools facilitated by advancing technology. This article focuses on the data collected in smart healthcare and the privacy concerns that arise from it. With the increasing adoption of technology in healthcare, there is a growing need to ensure the protection of sensitive health data. Anonymization techniques such as k-anonymity, l-diversity, and t-closeness are essential tools for safeguarding personal information. This paper discusses the importance of these techniques in maintaining privacy and security in the health environment and analyzes the privacy preserving in healthcare databases. It has been concluded that combinations of privacy models used have been successful in ensuring privacy of health data.