Exponential random graph estimation under differential privacy

@inproceedings{Lu2014ExponentialRG,
  title={Exponential random graph estimation under differential privacy},
  author={Wentian Lu and Gerome Miklau},
  booktitle={KDD},
  year={2014}
}
The effective analysis of social networks and graph-structured data is often limited by the privacy concerns of individuals whose data make up these networks. Differential privacy offers individuals a rigorous and appealing guarantee of privacy. But while differentially private algorithms for computing basic graph properties have been proposed, most graph modeling tasks common in the data mining community cannot yet be carried out privately. In this work we propose algorithms for privately… CONTINUE READING
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