NetSE: Medium: Privacy-Preserving Information Network and Services for Healthcare Applications

本研究探讨了发展隐私保护信息网络和服务（PPN）的挑战。下一代医疗保健信息系统和应用，如个性化和预测性医疗，需要PPN在独立的医疗保健提供者之间进行隐私保护信息共享和传播，使信息能够通过分布式访问控制内容进行访问，同时保护个人健康信息和个人医疗隐私免受未经授权的披露。这项研究的智力价值包括：（1）在分布式访问控制内容上的隐私保护搜索功能，这是PPN的关键功能;（2）一套实用感知数据匿名化服务，保护个人医疗信息的隐私，防止未经授权的披露，同时最大限度地提高医疗服务提供商的数据实用性;以及（3）针对高可用性、可伸缩性和故障恢复而优化的PPN架构和中间件。首先，这项研究将创造更好和更广泛的理解的挑战和功能要求，建立下一代的隐私保护网络信息系统的分布式访问控制的内容。将在PPN核心之上开发一个特定领域的概念验证原型，用于发现和分析耐药细菌感染的风险因素。这些真实世界的研究将与莫尔豪斯医学院和亚特兰大儿童保健中心合作进行，并将作为本研究的驱动因素和测试平台。其次，本研究将证明，PPN是一个使能的基础设施，实时，连续和按需数据分析的分布式和私有共享的数据存储库。

This research explores challenges in developing privacy-preserving information networks and services (PPNs). Next generation healthcare information systems and applications, such as personalized and predictive medicine, need PPNs for privacy-preserving information sharing and dissemination among independent healthcare providers, enabling information access over distributed access controlled content, while safeguarding personal health information and medical privacy of individuals from unauthorized disclosures.The intellectual merits of this research include the development of: (1) privacy-preserving search capabilities over distributed access controlled content, a critical functionality for PPNs; (2) a suite of utility-aware data anonymization services, preserving the privacy of personal medical information against unauthorized disclosure, at the same time maximizing the data utility for medical service providers; and (3) the PPN architecture and middleware optimized for high availability, scalability and failure recovery.The broad impact is two-fold. First, this research will create better and broader understanding of the challenges and functional requirements for building the next generation of privacy preserving networked information systems over distributed access controlled content. A domain-specific proof-of-concept prototype on top of the PPN core will be developed for discovering and analyzing risk factors for resistant bacterial infections. These real-world studies will be conducted in collaboration with Morehouse School of Medicine and Children's Healthcare of Atlanta, and be use as both a driver and a testbed for this research. Second, this research will demonstrate that the PPN is an enabling infrastructure for real-time, continuous and on demand data analysis over massively-distributed and privately-shared data repositories.