2022 Fiscal Year Final Research Report
Achieving Differential Privacy under Spatiotemporal Correlations
| Project/Area Number |
19K20269
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 60070:Information security-related
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| Research Institution | Hokkaido University (2022)
Kyoto University (2019-2021) |
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Principal Investigator |
Cao Yang 北海道大学, 情報科学研究院, 准教授 (60836344)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Keywords | 差分プライバシ / 時空間データ |
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| Outline of Final Research Achievements |
Differential Privacy has been extensively studied and deployed as the de facto privacy standard for preserving data privacy during collection and analysis. In this project, we demonstrate the potential risks and utility insufficiency of Differential Privacy when applied to spatiotemporal data. We propose new, flexible privacy notions for spatiotemporal data, such as Geo-graph-indistinguishability (DBSec 2019, IEICE 2023), Spatiotemporal Event Privacy (IEEE ICDE 2019, IEEE TKDE 2019), and Policy-based Location Privacy (ESORICS 2020) to achieve a better privacy-utility tradeoff.
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| Free Research Field |
データベース
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| Academic Significance and Societal Importance of the Research Achievements |
時空間データの収集と分析は、多くの研究分野や新興産業の基盤となっており、例えば、スマートシティ、交通予測、人流統計、クラウドソーシング、自動運転などがあります。しかし、プライバシーは無視できない障壁となることが多いです。本研究の成果は、時空間データ駆動型の科学技術の発展を支援することができます。
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