Shin Hyunah, Ryu Kyeongmin, Kim Jong-Yeup, Lee Suehyun
Department of Healthcare Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea.
Department of Otorhinolaryngology-Head and Neck Surgery, Konyang University College of Medicine, Daejeon, Republic of Korea.
Digit Health. 2024 Nov 4;10:20552076241282242. doi: 10.1177/20552076241282242. eCollection 2024 Jan-Dec.
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.
随着大数据时代的到来，数据安全问题日益普遍。医疗保健机构有更多数据可用于分析，但由于无法防止数据泄露，它们每年都会遭受损失。为了克服这些挑战，医疗保健领域中数据保护技术的应用研究正在积极开展，特别是对诸如谷歌宣布的联邦学习和最近备受关注的区块链技术等前沿技术的研究。为了了解这些研究成果，我们探究了最广泛使用的隐私技术的研究、方法和局限性。在调查了2017年至2023年间发表的相关论文并确定了最新技术趋势后，我们挑选了相关论文并对相关技术进行了综述。在此过程中，四项技术成为本研究的重点：区块链、联邦学习、同构加密和差分隐私。总体而言，我们的分析通过指出当前隐私技术的局限性并提出未来研究方向，为研究人员提供了对隐私技术研究的见解。
