Computer Science Department, King Saud University, Riyadh, Kingdom of Saudi Arabia.
Nanjing Institute of Technology, Nanjing, China.
Math Biosci Eng. 2021 Sep 2;18(6):7539-7560. doi: 10.3934/mbe.2021373.
Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.
移动医疗网络（MHNWs）为患者提供了即时的医疗保健和远程健康监测。目前，需要快速收集、处理和分析大量的健康数据。这样做的主要障碍是 MHNWs 需要的计算存储资源有限。因此，健康数据必须外包给云。虽然云具有强大的计算能力和密集的存储资源的优势，但存在安全和隐私问题。因此，我们的研究探讨了如何安全有效地收集和聚合这些健康数据，重点关注聚合数据的理论重要性和应用潜力。在这项工作中，我们提出了一种新颖的基于云的私有容错数据聚合方案设计。我们的设计基于未来的密文机制，以提高 MHNWs 的容错能力。我们的方案通过差分隐私进行私有化，通过加密有噪声的健康数据并使云只能获得有噪声的总和的结果来实现。我们的方案高效、可靠且安全，结合了不同的方法和算法来提高系统的安全性和效率。我们的方案通过广泛的模拟研究进行了评估，模拟结果表明它是高效和可靠的。与相关方案相比，我们的方案的计算成本显著降低。相关方案中的聚合误差从${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$最小化为我们方案中的 O(1)。
