SCH: EXP: Collaborative Research: Privacy-Preserving Framework for Publishing Electronic Healthcare Records

SCH：EXP：合作研究：发布电子医疗记录的隐私保护框架

• 批准号: 1836945
• 负责人: Heng Huang
• 金额: $ 4.26万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836945&HistoricalAwards=false
• 关键词: SCH; EXP; Collaborative; Research; Privacy

This project builds a novel privacy-preserving framework with both new algorithms and software tools to: 1) evaluate the effectiveness of current identifier-suppression techniques for Electronic Healthcare Record (EHR) data; 2) de-identify and anonymize EHR data to protect personal information without significantly reducing the utility of data for secondary data analysis. The proposed techniques eliminate the violation of privacy through re-identification, and facilitate the secondary usage, sharing, publishing and exchange of healthcare data without the risk of breaching protected health information (PHI). This new privacy-preserving framework injects the ICD-9-CM-aware constraint-based privacy-preserving techniques into EHRs to eliminate the threat of identifying an individual in the secondary use of research data. The proposed technique and development can be readily adapted to other types of healthcare databases in order to ensure privacy and prevent re-identification of published data. The project produces groundbreaking algorithms and tools for identifying privacy leakages and protecting personal privacy information in EHRs to improve healthcare data publishing. New privacy-preserving techniques developed in this project lead towards a new type of healthcare science for EHRs. The project also delivers fundamental advancements to engineering by showing how to integrate biomedical domain knowledge with a computationally advanced quantitative framework for preserving the privacy of published EHRs. HIPAA has established protocols and industry standards to protect the confidentiality of PHI. However, our results demonstrate that, even with regard to health data that meets HIPAA requirements, the risk of re-identification is not completely eliminated. By identifying the security vulnerabilities inherent in the HIPAA standards, our research develops a more rigorous security standard that greatly improves privacy protections by applying state-of-the-art algorithms. The developed data privacy-preserving framework has significant implications for the future of US healthcare data publishing and related applications. Specifically, the transition from paper records to EHRs has accelerated significantly since the passage of the HITECH Act of 2009. The Act provides monetary incentives for the "meaningful use" of EHRs. As a result, the quality and quantity of healthcare databases has risen sharply, which has renewed the public's fear of a breach of privacy of their medical information. This research work is innovative and crucial not only for facilitating EHR data publishing, but also for enhancing the development and promotion of EHRs. At the educational front, this project facilitates the development of novel educational tools to construct entirely new courses and laboratory classes for healthcare, data privacy, data mining, and a wide range of applications. As a result, it enhances the current instructional methods for teaching data privacy and data mining, and has compelling biomedical and healthcare applications that can facilitate learning of computational algorithms. This project involves both undergraduate and graduate students in the three participating institutions. The PIs make a strong effort to engage minority graduate and undergraduate students in research activities in order to increase their exposure to cutting-edge research.

该项目通过新算法和软件工具构建了一种新颖的隐私框架：1）评估当前的标识符支持技术对电子保健记录（EHR）数据的有效性； 2）取消识别并匿名化EHR数据，以保护个人信息，而无需显着降低数据的实用性以进行辅助数据分析。拟议的技术通过重新识别消除了侵犯隐私的侵犯，并促进了次要使用，共享，出版和交换医疗保健数据，而没有违反受保护的健康信息（PHI）的风险。这个新的隐私保护框架将基于ICD-9-CM感知约束的隐私保护技术注入EHR，以消除在研究数据中识别个人的威胁。提出的技术和开发很容易适应其他类型的医疗保健数据库，以确保隐私并防止重新识别已发布的数据。该项目生产了开创性的算法和工具，以识别隐私泄漏并保护EHR中的个人隐私信息以改善医疗保健数据出版。在该项目中开发的新的隐私技术导致了针对EHRS的新型医疗科学。该项目还通过展示如何将生物医学领域知识与计算先进的定量框架整合到维护已发表的EHR的隐私方面，从而为工程提供了根本的进步。 HIPAA建立了协议和行业标准，以保护PHI的机密性。但是，我们的结果表明，即使在满足HIPAA要求的健康数据方面，重新识别的风险也无法完全消除。通过确定HIPAA标准固有的安全漏洞，我们的研究开发了更严格的安全标准，可以通过应用最先进的算法来大大改善隐私保护。开发的数据隐私保护框架对美国医疗保健数据发布和相关应用程序的未来具有重要意义。具体而言，自2009年《 HITECH法案》通过以来，从纸质记录到EHRS的过渡已大大加速。该法案为EHRS的“有意义使用”提供了货币激励措施。结果，医疗保健数据库的质量和数量急剧上升，这使公众更加担心违反隐私的医疗信息。这项研究工作不仅对于促进EHR数据出版而创新和至关重要，而且对于增强EHR的发展和促进。在教育方面，该项目有助于开发新颖的教育工具，以建造全新的课程和实验室课程，用于医疗保健，数据隐私，数据挖掘以及广泛的应用。结果，它增强了当前的教学方法，用于教授数据隐私和数据挖掘，并具有令人信服的生物医学和医疗保健应用程序，可以促进计算算法的学习。该项目涉及三个参与机构的本科和研究生。 PI竭尽全力使少数族裔毕业生和本科生从事研究活动，以增加他们对尖端研究的接触。