2019 Fiscal Year Final Research Report
Security Enhancement for MapReduce and Its Application to Spatio-Temporal Databases
Project/Area Number |
16K00155
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Multimedia database
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Research Institution | Hiroshima University |
Principal Investigator |
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Project Period (FY) |
2016-04-01 – 2020-03-31
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Keywords | マップリデュース / 秘密計算 / 準同形暗号 / スカイライン問合せ / Map Reduce / Secure Computation / Homomorphic Cryptography / Skyline Query |
Outline of Final Research Achievements |
We have studied security enhancements for MapReduce (MR), a popular parallel distributed computation framework for processing “big data.” Many essential data mining functions have been implemented in MR and are used for analyzing big data. We proposed secure computation methods of some data mining functions by using a secure protocol. In the study, we considered the secure protocol by using secure computation techniques and homomorphic cryptography, which made it possible to analyze sensitive data more securely without losing MR’s efficiency.
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Free Research Field |
知能情報学
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Academic Significance and Societal Importance of the Research Achievements |
MR計算を行う機器がローカルネットワーク内のコンピュータで構成されている現時点では,「分散計算過程におけるデータの機密性」はあまり大きな問題とならないが,今後,ビッグデータがさらに発展してゆくと,今以上に広域に分散計算を行う必要性が生じると見込まれ,分散計算における機密性強化は重要な問題となる.また,提案した方法を使用することで,プライバシーを保護したまま,他の組織のデータベースとの知識共有が可能となるため保有するデータベースの潜在的価値も高めることができる.
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