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

安全可靠的灾难场景实时信息服务数据管理与分析平台

• 批准号: 19K12122
• 负责人: 王 軍波
• 金额: $ 2.75万
• 依托单位: The University of Aizu
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2019
• 资助国家: 日本
• 起止时间: 2019-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-19K12122/
• 关键词: Big Data Analysis; Emergency Scenario; Security; Encypted Searching; Streaming Big Data; Encrypted Searching; Data Management; Privacy

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.

在项目实施的第一年，我们对应急或灾难场景下的大数据分析、数据处理过程中的安全问题等进行了研究，主要成果包括：（1）雾计算环境下应急场景下的大数据分析：研究了雾计算支持下的空间大数据处理。我们分析了空间聚类的过程，这是一个典型的空间数据分析类别，并提出了一个架构，将数据处理集成到雾计算。通过对熊本地震期间收集的真实的数据的评估，我们已经确定，所提出的解决方案显着优于其他解决方案。（2）数据处理过程中的安全性：最近研究了基于Blackchain的存储系统（BSS），其可以以安全和分布式的方式存储敏感信息。在BSS中，矿工被假设部署在广泛的区域中，类似于边缘计算环境中的本地节点，并且他们在收集足够的数据后生成块。今年，我们研究了区块链和大数据处理的整合，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，这需要两个服务器进行交互和加密的不确定数据，我们使用授权排名的方法来计算KNN。