2020 Fiscal Year Research-status Report
Achieving Differential Privacy under Spatiotemporal Correlations
| Project/Area Number |
19K20269
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| Research Institution | Kyoto University |
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Principal Investigator |
曹 洋 京都大学, 情報学研究科, 特定助教 (60836344)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Keywords | Differential Privacy / location privacy / spatiotemporal data |
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| Outline of Annual Research Achievements |
We developed a policy-based location privacy model, called PGLP, with both flexibility and rigorousness. We have proved that our model is a generalization of existing state-of-the-art location privacy models such as Geo-indistinguishability
(ACM CCS13) and delta-location set privacy (ACM CCS15). The flexibility is benefited with the customized "policy graph", which is a graph that defines what needs to be protected (i.e., satisfying indistinguishability) and what is not. One can specify suitable policy graph for better privacy-utility trade-off in a her application scenario.
Based on our techniques, we also design a privacy-preserving location privacy enhanced pandemic analysis and contact tracing prototype to help combat COVID-19.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
Our main work was accepted in a prestigious conference in security, ESORICS 2020.
Our work on privacy-preserving epidemic surveillance was published as a demonstration paper in VLDB 2020.
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| Strategy for Future Research Activity |
We will continue to study the connection between spatiotemporal correlation and differential privacy by exploring the user-user correlations.
Also, we will investigate how to apply our techniques in supporting people's normal life in the post-covid era.
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