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On Learning Cluster Coefficient of Private Networks
Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. I...
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| Main Authors: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2012
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3889125/ https://ncbi.nlm.nih.gov/pubmed/24429843 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/ASONAM.2012.71 |
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