Privacy Risks in Health Big Data: A Systematic Literature Review

TL;DR

This paper systematically reviews privacy risks in health big data, analyzing current security challenges and exploring advanced technologies like homomorphic encryption, blockchain, and federated learning to enhance data privacy.

Contribution

It provides a comprehensive overview of privacy issues in health big data and discusses innovative security solutions and future research directions.

Findings

Identification of key privacy challenges in health big data

Analysis of advanced privacy-preserving technologies

Proposal of a future research framework for health data security

Abstract

The digitization of health records has greatly improved the efficiency of the healthcare system and promoted the formulation of related research and policies. However, the widespread application of advanced technologies such as electronic health records, genomic data, and wearable devices in the field of health big data has also intensified the collection of personal sensitive data, bringing serious privacy and security issues. Based on a systematic literature review (SLR), this paper comprehensively outlines the key research in the field of health big data security. By analyzing existing research, this paper explores how cutting-edge technologies such as homomorphic encryption, blockchain, federated learning, and artificial immune systems can enhance data security while protecting personal privacy. This paper also points out the current challenges and proposes a future research framework in this key area.