Encryption methods and software for privacy-preserving analysis of biomedical data

用于生物医学数据隐私保护分析的加密方法和软件

• 批准号: 9357584
• 负责人: HAIXU TANG
• 金额: $ 46.14万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9357584
• 关键词: Algorithms; Awareness; Beavers; Big Data; Biomedical Computing; Biomedical Research; Collaborations; Collection; Computer software; Custom; Data; Data Science; Development; Devices; Diagnostic; Dimensions; Electronic Health Record; Ensure; Foundations; Genomics; Heterogeneity; Human; Human Genome; Image; Information Technology; Institution; Lead; Libraries; Measures; Medical Imaging; Methods; Mining; Outsourcing; Patients; Performance; Play; Precision Medicine Initiative; Privacy; Protocols documentation; Research Personnel; Science; Secure; Security; Software Tools; Statistical Algorithm; Statistical Data Interpretation; Suggestion; Techniques; Testing; Therapeutic; base; big biomedical data; biomedical informatics; cryptography; data mining; encryption; genome analysis; high dimensionality; high risk; improved; indexing; individual patient; mHealth; multidisciplinary; next generation sequencing; novel; open source; personalized medicine; privacy protection; prospective; prototype; software development; tool; usability; vector

Project summary The effective and efficient utilization of the big data accumulated in biomedical sciences, including the genomic, imaging data collected from patients that are often integrated with their electronic health records represents a great opportunity as well as a big challenge for the biomedical data science. Because biomedical data are collected from individual patients, and thus carry identifiable information of the data donors, the projection of their privacy becomes an important concern in large-scale projects including the recently launched Precision Medicine Initiative. In the past few years, significant progresses have been made on cryptographic techniques, including the homomorphic encryption (HME) that enables a direct analysis of encrypted data without decrypting it, and the Secure Multiparty Computing (SMC) that allows two or more organizations to jointly compute a task without exposing to each other’s inputs. Here, based on these techniques, we propose to develop a suite of encryption protocols and open-source software tools that can be used by biomedical researchers in a plug-and-play manner for the statistical analysis of encrypted biomedical data. We note that our methods assume biomedical data will be protected by encryption once they are generated, and the subsequent analysis and sharing will always be performed on the encrypted form, which thus can achieve a high security standard for privacy protection.

项目摘要 生物医学科学中积累的大数据的有效利用， 包括基因组，从经常与患者收集的成像数据 他们的电子健康记录代表了一个很好的机会，也是一个巨大的挑战 用于生物医学数据科学。因为从个人那里收集生物医学数据 患者，因此携带可识别的数据捐赠者的信息，他们的投影 隐私成为大规模项目的重要问题 发起了精密医学计划。在过去的几年中，取得了重大进展 是在加密技术上制作的，包括同态加密（HME） 这样可以直接分析加密数据而不解密它，并且安全 多方计算（SMC）允许两个或多个组织共同计算 任务而无需暴露于彼此的输入。在这里，根据这些技术，我们 提出开发一套加密协议和开源软件工具的建议 生物医学研究人员可以以插件的方式用于统计 分析加密的生物医学数据。我们注意到我们的方法假设生物医学 一旦生成数据，数据将受到加密的保护，随后的 分析和共享将始终以加密形式进行，因此可以 实现高安全标准以保护隐私。