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

该项目使用新算法和软件工具构建了一个新颖的隐私保护框架，以：1）评估当前电子医疗记录（EHR）数据的标识符抑制技术的有效性; 2）对EHR数据进行去标识和匿名化，以保护个人信息，而不会显着降低数据用于二级数据分析的效用。所提出的技术通过重新识别消除了对隐私的侵犯，并促进了医疗保健数据的二次使用、共享、发布和交换，而没有违反受保护的健康信息（PHI）的风险。这种新的隐私保护框架将ICD-9-CM感知的基于约束的隐私保护技术注入到EHR中，以消除在研究数据的二次使用中识别个体的威胁。所提出的技术和开发可以很容易地适应于其他类型的医疗保健数据库，以确保隐私和防止重新识别已发布的数据。该项目产生了开创性的算法和工具，用于识别隐私泄露和保护EHR中的个人隐私信息，以改善医疗保健数据发布。在这个项目中开发的新的隐私保护技术导致了EHR的新型医疗保健科学。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相结合，以保护已发布的EHR的隐私，从而为工程提供了根本性的进步。HIPAA已经建立了协议和行业标准来保护PHI的机密性。然而，我们的研究结果表明，即使是符合HIPAA要求的健康数据，重新识别的风险也没有完全消除。通过识别HIPAA标准中固有的安全漏洞，我们的研究开发了一个更严格的安全标准，通过应用最先进的算法大大提高了隐私保护。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，从纸质记录到电子健康记录的过渡大大加快。该法为“有意义地使用”电子健康记录提供了金钱奖励。因此，医疗保健数据库的质量和数量急剧上升，这使公众重新担心他们的医疗信息隐私受到侵犯。这项研究工作是创新的，不仅对促进电子健康记录数据发布，而且对促进电子健康记录的发展和推广至关重要。在教育方面，该项目促进了新型教育工具的开发，为医疗保健，数据隐私，数据挖掘和广泛的应用构建全新的课程和实验室课程。因此，它增强了当前用于教授数据隐私和数据挖掘的教学方法，并具有引人注目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生和研究生。该PI使一个强大的努力，从事研究活动的少数民族研究生和本科生，以增加他们接触到尖端的研究。

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.