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

这项研究探索了在开发隐私保护信息网络和服务(PPN)方面的挑战。下一代医疗信息系统和应用，如个性化和预测医学，需要个人专用网在独立的医疗服务提供者之间共享和传播隐私保护信息，允许分布式访问控制内容上的信息访问，同时保护个人健康信息和个人的医疗隐私免受未经授权的披露。本研究的智力价值包括：(1)对分布式访问控制内容的隐私保护搜索能力，这是个人专用网的关键功能；(2)一套公用事业感知的数据匿名化服务，保护个人医疗信息的隐私免受未经授权的披露，同时最大化医疗服务提供商的数据效用；以及(3)针对高可用性、可扩展性和故障恢复进行了优化的PPN体系结构和中间件。首先，这项研究将更好和更广泛地理解在分布式访问控制内容上构建下一代隐私保护网络信息系统所面临的挑战和功能要求。将在PPN核心之上开发一个特定领域的概念验证原型，以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亚特兰大莫尔豪斯医学院和儿童保健学院合作进行，并作为这项研究的驱动力和试验床。其次，这项研究将证明，PPN是一种能够在大规模分布和私人共享的数据储存库上进行实时、连续和按需数据分析的基础设施。

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.