Safe and Secure Data Management and Analytics Platform for Real-time Information Service in Disaster Scenarios

在项目的第一年，我们研究了应急或灾难场景的大数据分析，数据处理过程中的安全问题等。主要研究成果包括：(1)雾计算环境下应急场景大数据分析：研究了基于雾计算的空间大数据处理。空间聚类是空间数据分析的一个典型类别，我们分析了空间聚类的过程，并提出了一种将数据处理集成到雾计算中的架构。通过对熊本地震期间收集的真实数据进行评估，我们确定提出的解决方案明显优于其他解决方案。(2)数据处理过程中的安全性：基于黑链的存储系统（BSS）能够以安全、分布式的方式保存敏感信息，是目前研究的热点。在BSS中，矿工被假设部署在一个广泛的区域，类似于边缘计算环境中的本地节点，他们在收集到足够的数据后生成区块。在这一年里，我们研究了区块链与大数据处理的融合，并提出了一种算法来优化系统中的资源。(3)加密搜索：针对存储在半可信云服务器中的不确定数据，设计了一种高效安全的K近邻（KNN）查询方案。我们采用改进的同态加密，该加密需要两台服务器交互并加密不确定数据，并使用授权秩方法计算KNN。

In the first year of the project, we have researched on big data analysis for emergency or disaster scenarios, security issues in the data processing procedure and so on. The main results include:(1) Big data analysis for emergency scenarios in fog-computing environment: We study fog-computing supported spatial big data processing. We analyze the process for spatial clustering, which is a typical category for spatial data analysis, and propose an architecture to integrate data processing into fog computing. Through evaluation on real data collected during Kumamoto earthquake, we have determined that the proposed solution significantly outperforms other solutions.(2)Security in the data processing procedure: Blackchain-based storage systems (BSS) are investigated recently, which can save sensitive information in secure and distributed way. In a BSS, miners are assumed to be deployed in a broad area, similar with local nodes in the edge computing environment, and they generate blocks after collecting enough data. In this year, we have studied the integration of Blockchain and Big Data processing and propose an algorithm to optimize the resource in the system.(3)Encrypted searching: We design an efficient and safe K nearest neighbor (KNN) query scheme for uncertain data stored in semi-trusted cloud servers. We apply the modified homomorphic encryption, which requires two servers to interact and encrypt the uncertain data, and we use the authorized rank method to compute KNN.