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

项目摘要 有效和高效地利用生物医学科学积累的大数据， 包括从患者收集的基因组成像数据，这些数据通常与 他们的电子健康记录是一个巨大的机遇，也是一个巨大的挑战， for the biomedical生物医学data数据science科学.因为生物医学数据是从个人收集的， 患者，并因此携带数据捐赠者的可识别信息，其 隐私成为大型项目中的一个重要问题， 启动精准医疗计划在过去的几年里，取得了重大进展。 密码技术，包括同态加密（HME） 它可以直接分析加密数据，而无需对其进行解密， 多方计算（SMC），允许两个或更多组织联合计算 不暴露于彼此的输入任务。基于这些技术，我们 我提议开发一套加密协议和开源软件工具 生物医学研究人员可以以即插即用的方式用于统计 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 数据一旦生成，将受到加密保护， 分析和共享将总是在加密的表单上执行，因此， 达到隐私保护的高安全标准。

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.