Publishing Graph Degree Distribution with Node Differential Privacy
Summary: Publishes a graph degree distribution under node-DP by projecting to lower sensitivity, using aggregation-based and cumulative-histogram methods. Reports strong accuracy gains over prior work; includes an introspective analysis of key factors. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wei-Yen Day (Purdue University)
- 2. Ninghui Li (Purdue University)
- 3. Min Lyu (University of Science and Technology Beijing)
BibTeX Citation
@inproceedings{day_sigmod16,
title = {{Publishing Graph Degree Distribution with Node Differential Privacy}},
author = {Day, Wei-Yen and Li, Ninghui and Lyu, Min},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2926745},
url = {https://dl.acm.org/doi/10.1145/2882903.2926745},
year = {2016}
}
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