2022 Fiscal Year Research-status Report
Secure, Precise and Fast Sequential Pattern Mining with Learning Data Distribution
| Project/Area Number |
21K17746
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Le Hieu・Hanh 東京工業大学, 情報理工学院, 助教 (60813996)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Keywords | data mining / privacy / medical data |
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| Outline of Annual Research Achievements |
This study aims to present a method for eliminating the need for trust in sequential pattern mining (SPM) while preserving privacy and providing secure, precise, and fast sequential data analysis which carefully learns the data distribution. The fundamental algorithms of sequential data analysis on sequential medical data without privacy-preserving have been studied this year. In detail, several methods to analyze the sequence variants from more than one hospital have been designed and evaluated. The basic privacy-preserving SPM has also been studied in detail and the initial experimental results have been observed. For estimating the frequency of the sequences, an appropriate amount of noise is added to the original frequency to ensure privacy.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
The basic algorithm has been developed. However, evaluation process has been delayed due to the constraint of using sensitive data.
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| Strategy for Future Research Activity |
The evaluation process will be improved by using other datasets which are easier to use.
Moreover, further improving the performance of the privacy-preserving data analysis will be studied.
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