Developing novel technologies that ensure privacy and security in biomedical data science research

开发确保生物医学数据科学研究隐私和安全的新技术

• 批准号: 10560470
• 负责人: Jaideep Vaidya
• 金额: $ 38.45万
• 依托单位: RUTGERS THE STATE UNIV OF NJ NEWARK
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10560470
• 关键词: Biomedical Research; Collaborations; Communities; Complex; Data; Data Analyses; Data Collection; Data Commons; Data Science; Diabetes Mellitus; Disease; Economics; Ensure; Goals; Healthcare; Institution; Lead; Learning; Malignant Neoplasms; Modality; Modeling; Modernization; Nature; Pathway interactions; Prevention; Privacy; Public Health; Research; Risk; Security; Software Tools; Statutes and Laws; Substance abuse problem; Techniques; Technology; Work; biomedical data science; care fragmentation; cohort; cost; data sharing; deep learning; improved; new technology; novel; open source; patient privacy; programs; public health intervention; public health research; social; success; tool; usability

Data science holds the promise of enabling new pathways to discovery and can improve the understanding, prevention and treatment of complex disorders such as cancer, diabetes, substance abuse, etc., which are significantly on the rise. The promise of data science can be fully realized only when collected data can be collaboratively shared and analyzed. However, the widespread increases in healthcare data breaches due to inappropriate access as well as the increasing number of novel privacy attacks restrict institutions from sharing data. Indeed, in some cases, the results of the analysis can themselves lead to significant privacy harm. The success of the data commons depends on ensuring the maximal access to data, subject to all of the patient privacy requirements including those mandated by legislation, and all of the constraints of the organization collecting the data itself. While there are existing solutions that can solve parts of the problem, there are significant challenges in truly incorporating these into comprehensive working solutions that are usable by the biomedical research community, and new challenges brought on by modern techniques such as deep learning. The long-term goal of this research is to develop technologies that can holistically enable data sharing while respecting privacy and security considerations and to ensure that they are implemented in existing platforms that have widespread acceptance in the research community. Towards this, the objective of this project is to develop complementary solutions for risk inference, distributed learning, and access control that can enable different modalities of data sharing. The problems studied are general in nature and will evolve depending on research successes and new impediments that arise. The proposed program of research is significant since lack of access to biomedical data can lead to fragmentation of care, resulting in higher economic and social costs, and is a significant impediment to biomedical research. The project will result in open-source, freely available software tools that will be integrated into widely used data collection, cohort identification, and distributed analytics platforms. There are several ongoing collaborations that will serve as initial pilot customers to provide use cases, identify the requirements, evaluate results, and in general validate the developed solutions.

数据科学有望带来新的发现途径，并可以改善 了解、预防和治疗复杂疾病，如癌症、糖尿病、 药物滥用等，其数量显着上升。数据科学的前景可以是 只有当收集的数据可以协作共享和分析时，才能充分实现。然而， 由于不适当的访问以及 越来越多的新型隐私攻击限制了机构共享数据。确实，在一些 在某些情况下，分析结果本身可能会导致严重的隐私损害。成功 数据共享的范围取决于确保最大限度地访问数据，并遵守所有患者的要求 隐私要求，包括立法规定的隐私要求以及所有限制 组织本身收集数据。虽然现有的解决方案可以解决部分问题 问题，真正将这些纳入综合工作中存在重大挑战 生物医学研究界可用的解决方案以及带来的新挑战 通过深度学习等现代技术。这项研究的长期目标是开发 可以在尊重隐私和安全的同时全面实现数据共享的技术 考虑因素并确保它们在现有平台中实施 研究界的广泛接受。为此，该项目的目标是 开发风险推断、分布式学习和访问控制的补充解决方案 可以实现不同模式的数据共享。研究的问题具有普遍性 并将根据研究的成功和出现的新障碍而发展。拟议的 研究计划意义重大，因为缺乏生物医学数据可能导致 护理分散化，导致更高的经济和社会成本，是一个重大问题 生物医学研究的障碍。该项目将开源、免费提供 软件工具将被集成到广泛使用的数据收集、队列识别和 分布式分析平台。有几项正在进行的合作将作为初步合作 试点客户提供用例、确定需求、评估结果，以及总体情况 验证开发的解决方案。