2023 Fiscal Year Annual Research Report
Scalable and Precise Social Network Algorithms under Local Differential Privacy
Publicly Offered Research
| Project Area | Creation and Organization of Innovative Algorithmic Foundations for Leading Social Innovations |
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| Project/Area Number |
23H04377
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| Research Institution | The University of Tokyo |
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Principal Investigator |
スッパキットパイサン ウォラポン 東京大学, 大学院情報理工学系研究科, 特任准教授 (30774103)
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| Project Period (FY) |
2023-04-01 – 2025-03-31
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| Keywords | Differential Privacy / Graph Algorithm |
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| Outline of Annual Research Achievements |
We conducted the following research on graph algorithms under local differential privacy.
(1) We complete the research to show that the result of the spectral clustering is not significantly changed even when the graph is obfuscated under the local differential privacy. The result is published at https://arxiv.org/abs/2309.06867.
(2) We present an algorithm that computes the number of paths and Katz centrality while adhering to local differential privacy standards. This work is among the first to incorporate global graph information into local differential privacy frameworks. The result is published at https://arxiv.org/abs/2310.14000. Additionally, the paper has been accepted through a peer review process and will be featured in the proceedings of UAI 2024.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We have conducted a research based on the plan, and have published the result at the refereed conference proceeding.
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| Strategy for Future Research Activity |
In fiscal year 2024, we have a plan to conduct the following two works:
(1) We will develop local clustering algorithms under local differential privacy. Compared to the spectral clustering algorithm which we have analyze in fiscal year 2023, we believe that we can obtain a better result when focusing on the task of local clustering.
(2) We will continue our work on subgraph counting problems. Specifically, we will work on the graph with small arboricity. While most of the current works focus on triangle counting, we plan to also work on the counting of larger subgraphs.
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