| Project/Area Number |
19K12122
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | The University of Aizu |
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Principal Investigator |
王 軍波 会津大学, コンピュータ理工学部, 准教授 (40646882)
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| 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)
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| Project Period (FY) |
2019-04-01 – 2020-03-31
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| Project Status |
Discontinued (Fiscal Year 2019)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | Big Data Analysis / Emergency Scenario / Security / Encypted Searching / Streaming Big Data / Encrypted Searching / Data Management / Privacy |
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| Outline of Research at the Start |
災害発生時においては時間の経過に伴って，人の移動や被災者からの救援要請など様々な状況が刻々と変化する．状況に関する人の移動と健康データをリアルタイムに分析し，変化する状況を迅速・的確に把握するのが非常重要な研究課題である．一方，近年健康などの個人情報を暗号したままで検索することが可能になるが，災害時の個人情報の適正的な管理，効率的な検索・分析が非常に挑戦的な研究課題である． 本研究は、二つの課題を同時に解決し、災害時安全・安心な解析基盤を提供する．
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| 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.
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