利用用户信任与移动性的电子健康敏感数据传播
E-Health Sensitive Data Dissemination Exploiting Trust and Mobility of Users
AI总结：
针对移动、动态拓扑和偶发交互下的电子健康紧急数据传播问题，本文提出基于社会信任与兴趣社区的 STEALTH 两阶段系统，实现高可靠、低延迟和高可用的敏感数据分发。
摘要：
电子健康服务处理大量敏感数据，因此需要可靠性与隐私保护。新技术的出现推动电子健康服务在传统医疗机构之外持续提供。这造成了不确定且不可靠的条件，从而带来控制用户敏感数据传播的挑战。此外，在需要快速响应的情形（例如心脏骤停）下，敏感数据传播仍存在空白。这要求网络在用户移动、动态网络拓扑以及设备之间偶发交互的情况下提供可靠的敏感数据传播。本文提出 STEALTH，这是一个利用社会信任和兴趣社区来应对这些挑战的系统。STEALTH 遵循两个步骤：聚类与传播。第一步中，STEALTH 根据用户的兴趣对设备进行分组，形成兴趣社区。医疗紧急事件启动第二步，其中 STEALTH 根据设备之间的信任级别，将用户敏感数据传播给属于特定社区的设备。仿真结果表明，STEALTH 确保将数据传播给能够为高效服务作出贡献的人员。STEALTH 在访问敏感数据方面实现了高达 97.14% 的可靠性，最大延迟为 170 ms，并在紧急情况下实现了高达 100% 的可用性。
英文摘要：
E-health services handle a massive amount of sensitive data, requiring reliability and privacy. The advent of new technologies drives e-health services into their continuous provision outside traditional care institutions. This creates uncertain and unreliable conditions, resulting in the challenge of controlling sensitive user data dissemination. Then, there is a gap in sensitive data dissemination under situations requiring fast response (e.g., cardiac arrest). This obligates networks to provide reliable sensitive data dissemination under user mobility, dynamic network topology, and occasional interactions between the devices. In this article, we propose STEALTH, a system that employs social trust and communities of interest to address these challenges. STEALTH follows two steps: clustering and dissemination. In the first, STEALTH groups devices based on the interests of their users, forming communities of interest. A healthcare urgency launches the second, in which STEALTH disseminates user sensitive data to devices belonging to specific communities, subjected to the level of trust between devices. Simulation results demonstrate that STEALTH ensures data dissemination to people who can contribute toward an efficient service. STEALTH has achieved up to 97.14% of reliability in accessing sensitive data with a maximum latency of 170 ms, and up to 100% of availability during emergencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
