用于医疗保健信息的实用程序感知数据匿名化模型

Fadi Alhaddadin, Jairo Gutiérrez
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引用次数: 0

摘要

使用收集到的数据是一种有价值的分析来源,有利于医学研究和实践。信息隐私被认为是一个重大的挑战,它阻碍了这些信息用于研究目的。在研究方面,出于研究目的而发布患者信息可能会在各种情况下导致患者隐私被侵犯。个别患者在使用其健康信息进行研究时可能不希望被识别。这项工作提出了一种实用感知数据匿名化模型,用于以保护隐私的方式共享患者健康信息。所建议的模型是交互式的,涉及在根据数据用户(研究者)指定的某些要求将患者信息发布用于研究目的之前对其进行的一系列操作。
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Utility-Aware Data Anonymization Model for Healthcare Information
The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients’ information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients’ health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients’ information before releasing it for research purposes according to certain requirements specified by the data user (researcher).
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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