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

该项目使用新的算法和软件工具构建了一个新的隐私保护框架，以：1)评估当前电子医疗记录(EHR)数据的识别符抑制技术的有效性；2)在不显著降低数据用于二次数据分析的情况下，对EHR数据进行去识别和匿名化，以保护个人信息。建议的技术通过重新识别消除了对隐私的侵犯，并促进了医疗数据的二次使用、共享、发布和交换，而不存在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，以消除在二次使用研究数据时识别个人身份的威胁。建议的技术和开发可以很容易地适用于其他类型的医疗保健数据库，以确保隐私并防止对已发布数据的重新识别。该项目产生了突破性的算法和工具，用于识别EHR中的隐私泄漏和保护个人隐私信息，以改进医疗数据发布。该项目开发的新的隐私保护技术为EHR带来了一种新型的医疗保健科学。该项目还通过展示如何将生物医学领域的知识与计算先进的量化框架相结合，以保护已公布的EHR的隐私，从而为工程提供了根本性的进步。HIPAA制定了协议和行业标准来保护PHI的机密性。然而，我们的结果表明，即使对于符合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标准固有的安全漏洞，我们的研究开发了一种更严格的安全标准，通过应用最先进的算法极大地改善了隐私保护。开发的数据隐私保护框架对美国医疗数据发布和相关应用的未来具有重要影响。具体地说，自2009年HITECH法案通过以来，从纸质记录向EHR的过渡大大加快了。该法案为“有意义地使用”电子健康记录提供了货币奖励。因此，医疗保健数据库的质量和数量都大幅上升，这再次引发了公众对侵犯其医疗信息隐私的担忧。这项研究工作具有创新性，不仅对促进电子健康记录数据的发布，而且对促进电子健康记录的发展和推广都具有重要意义。在教育方面，该项目促进了新型教育工具的开发，以构建全新的医疗保健、数据隐私、数据挖掘和广泛应用程序的课程和实验室课程。因此，它增强了当前教授数据隐私和数据挖掘的教学方法，并拥有引人注目的生物医学和保健应用程序，可以促进计算算法的学习。该项目涉及三个参与院校的本科生和研究生。私人投资机构大力吸引少数族裔研究生和本科生参与研究活动，以增加他们对尖端研究的接触。

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.