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A Privacy-Preserved Analytical Method for eHealth Database with Minimized Information Loss

机译:具有最小信息丢失的eHealth数据库的隐私保护分析方法

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摘要

Digitizing medical information is an emerging trend that employs information and communication technology (ICT) to manage health records, diagnostic reports, and other medical data more effectively, in order to improve the overall quality of medical services. However, medical information is highly confidential and involves private information, even legitimate access to data raises privacy concerns. Medical records provide health information on an as-needed basis for diagnosis and treatment, and the information is also important for medical research and other health management applications. Traditional privacy risk management systems have focused on reducing reidentification risk, and they do not consider information loss. In addition, such systems cannot identify and isolate data that carries high risk of privacy violations. This paper proposes the Hiatus Tailor (HT) system, which ensures low re-identification risk for medical records, while providing more authenticated information to database users and identifying high-risk data in the database for better system management. The experimental results demonstrate that the HT system achieves much lower information loss than traditional risk management methods, with the same risk of re-identification.
机译:医疗信息数字化是一种新兴趋势,它采用信息和通信技术(ICT)来更有效地管理健康记录,诊断报告和其他医疗数据,以提高医疗服务的整体质量。但是,医疗信息是高度机密的,并且涉及私人信息,即使合法访问数据也会引起隐私问题。医疗记录按需要提供健康信息以进行诊断和治疗,并且该信息对于医学研究和其他健康管理应用也很重要。传统的隐私风险管理系统专注于降低重新识别风险,并且它们不考虑信息丢失。另外,这样的系统不能识别和隔离携带侵犯隐私的高风险的数据。本文提出了一种Hiatus Tailor(HT)系统,该系统可确保较低的医疗记录重新识别风险,同时为数据库用户提供更多经过身份验证的信息,并识别数据库中的高风险数据,以实现更好的系统管理。实验结果表明,与传统的风险管理方法相比,HT系统实现的信息损失要低得多,并且具有相同的重新识别风险。

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