| 医学数据共享隐私保护中基于聚类的匿名化算法关键技术研究 |
| 修订日期：2022-12-20 点此下载全文 |
| 引用本文：唐明坤,吴思竹,周佳茵,等.医学数据共享隐私保护中基于聚类的匿名化算法关键技术研究[J].医学信息学杂志,2023,44(6):65-71, 78 |
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| 基金项目:国家重点研发计划(项目编号:2021YFC27013 01)；中国医学科学院医学与健康科技创新工程项目(项目编号:2021-I2M-1-057)。 |
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| 中文摘要:目的/意义 基于聚类的匿名化算法具有灵活性较高、适用范围较广、能够保留原始数据更多信息的特点。合理使用基于聚类的匿名化算法进行匿名化处理可以获得满足隐私保护需求的高质量医学数据。方法/过程 通过文献调研法和比较分析法,梳理面向医学数据共享、基于聚类的匿名化算法关键技术,概述该类算法的主要流程,归纳与之相关的隐私模型,包括具有代表性的传统隐私模型和个性化隐私模型,并分析代表性算法的优点和不足。结果/结论 应当合理选择基于聚类的匿名化算法类型、灵活改进算法模型,加大算法工具研发力度,以推动医学数据安全便利和高质量共享。 |
| 中文关键词:数据共享 隐私保护 聚类算法 数据匿名化 隐私模型 |
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| Study on Key Technologies of Clustering-based Anonymization Algorithm in Medical Data Sharing Privacy Protection |
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| Abstract:Purpose/Significance The anonymization algorithm based on clustering has the characteristics of high flexibility, wide application range, and the ability to retain more information of the original data. Reasonable use of clustering-based anonymization algorithm for anonymization can obtain high-quality medical data that meets the needs of privacy protection. Method/Process Through literature research and comparative analysis, the study sorts out the key technologies of clustering-based anonymization algorithm for medical data sharing, summarizes the main process of such algorithm and the related privacy models, including representative traditional privacy models and personalized privacy models, and analyzes the advantages and disadvantages of representative clustering-based anonymization algorithm. Result/Conclusion The type of clustering-based anonymization algorithms should be reasonably selected, the model of clustering-based anonymization algorithms should be flexibly improved, and the research and development of clustering-based anonymization tools should be heavily invested. These measures can promote safe, convenient and higher quality sharing of medical data. |
| keywords:data sharing privacy protection clustering algorithm data anonymization privacy model |
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