2019 Fiscal Year Annual Research Report
Safe and Secure Data Management and Analytics Platform for Real-time Information Service in Disaster Scenarios
| Project/Area Number |
19K12122
|
|---|
| Research Institution | The University of Aizu |
|---|
Principal Investigator |
王 軍波 会津大学, コンピュータ理工学部, 准教授 (40646882)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
SU Chunhua 会津大学, コンピュータ理工学部, 上級准教授 (40716966)
|
|---|
| Project Period (FY) |
2019-04-01 – 2020-03-31
|
| Keywords | Big Data Analysis / Emergency Scenario / Security / Encypted Searching |
|---|
| Outline of Annual Research Achievements |
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.
|
Research Products
(1 results)
