Application of privacy protection technology to healthcare big data

Abstract

With the advent of the big data era, data security issues are becoming more common. Healthcare organizations have more data to use for analysis, but they lose money every year due to their inability to prevent data leakage. To overcome these challenges, research on the use of data protection technologies in healthcare is actively underway, particularly research on state-of-the-art technologies, such as federated learning announced by Google and blockchain technology, which has recently attracted attention. To learn about these research efforts, we explored the research, methods, and limitations of the most widely used privacy technologies. After investigating related papers published between 2017 and 2023 and identifying the latest technology trends, we selected related papers and reviewed related technologies. In the process, four technologies were the focus of this study: blockchain, federated learning, isomorphic encryption, and differential privacy. Overall, our analysis provides researchers with insight into privacy technology research by suggesting the limitations of current privacy technologies and suggesting future research directions.

Keywords: Blockchain; differential privacy; federated learning; healthcare; homomorphic encryption privacy protection.

Figure 1.

A schematic diagram of keyword calculation through word counting.

Figure 2.

Flow of research for systematic literature review.

Figure 3.

Personal information protection technology trends (2013–2023 in PubMed).

Figure 4.

Conceptual schematic diagram of blockchain.

Figure 5.

Conceptual diagram of the federated learning technology.

Figure 6.

Conceptual diagram of the DP technology.

Figure 7.

Conceptual diagram of the homomorphic encryption technology.