| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| 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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