Privacy-Preserving Biomedical Data Sharing

电子健康记录(EHR)系统的采用加快了健康数据的收集和共享。EHR数据库通常与生物信息库集成并共享，以促进全基因组关联研究(GWAs)。虽然生物医学数据的这种整合和共享有许多社会效益(例如，个性化医疗)，但它也引起了对个人隐私的担忧。最近一项关于隐私攻击的调查展示了侵犯生物医学数据隐私的各种技术，这些技术质疑目前实践中的保护机制的有效性。*这项研究的目的是在保护个人隐私的同时，了解、评估和促进生物医学数据共享的问题。本次调研活动围绕三个相关课题展开。第一个任务是更充分地了解共享生物医学数据带来的隐私威胁和挑战，并提出适当的隐私模式。挑战是阻止不同数据类型之间的推断导致的隐私攻击，并量化给定对手模型的数据共享风险。第二项任务是开发具有强大隐私保障的数据共享机制。挑战是在隐私和数据效用之间提供一个合理的权衡。最后的任务是开发分布式机制，以安全地整合来自多个机构的数据，以促进受控和有效的数据共享。挑战是提出一种可扩展的算法，以保护存储在多个机构的原始数据免受不可信的数据聚合器的攻击。*生物医学研究的成功在很大程度上依赖于大型数据集的可用性。目前，还没有关于共享生物医学数据的具体国家指导方针。这项研究的结果将通过提供明确的指导和技术解决方案来帮助弥合这一差距，并将有助于增加公众对生物医学研究的信任。此外，这项研究将推进当前最先进的隐私保护数据共享技术，并拓宽我们对不同类型数据互联所造成的隐私威胁的理解。

The adoption of Electronic Health Record (EHR) systems has accelerated the collection and sharing of health data. EHR databases are often integrated with biorepositories and shared to facilitate genome-wide association studies (GWAS). While such integration and sharing of biomedical data have many social benefits (e.g., personalized medicine), it also raises concerns about individual privacy. A recent survey on privacy attacks demonstrates various techniques for breaching biomedical data privacy, and these techniques question the efficacy of the protection mechanisms that are currently in practice. ******The aim of this research is to understand, evaluate and contribute to the problem of sharing biomedical data while protecting individual privacy. The research activities are organized along the three related tasks. The first task is to more fully understand the privacy threats and challenges of sharing biomedical data, and to propose appropriate privacy models. The challenge is to thwart privacy attacks caused by inferences between different data types, and to quantify the risk of data sharing for a given adversary model. The second task is to develop data-sharing mechanisms with strong privacy guarantees. The challenge is to provide a reasonable trade-off between privacy and data utility. The final task is to develop distributed mechanisms to securely integrate data from multiple institutions to facilitate controlled and effective data sharing. The challenge is to propose a scalable algorithm that safeguards the raw data stored across multiple institutions against an untrusted data aggregator.******The success of biomedical research heavily depends on the availability of large datasets. At present, there is no specific national guideline for sharing biomedical data. The outcomes from this research will help to bridge this gap by providing clear guidance and technical solutions, and will help increase public trust in biomedical research. Further, this research will advance the current state-of-the-art techniques for privacy-preserving data sharing and broaden our understanding of the privacy threats caused by the interconnections of different types of data.