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STATISTICAL RELATIONAL LEARNING GROUP @ UMD

Preserving the Privacy of Sensitive Relationships in Graph Data

Proceedings of the First SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007), Volume 4890, page 153-171 - March 2008

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In this paper, we focus on the problem of preserving the privacy of sensitive relationships in graph data. We refer to the problem of inferring sensitive relationships from anonymized graph data as link re-identification. We propose five different privacy preservation strategies, which vary in terms of the amount of data removed (and hence their utility) and the amount of privacy preserved. We assume the adversary has an accurate predictive model for links, and we show experimentally the success of different link re-identification strategies under varying structural characteristics of the data.

BibTex references

@InProceedings\{zheleva:kdd07-lncs,
  author       = "Zheleva, Elena and Getoor, Lise",
  title        = "Preserving the Privacy of Sensitive Relationships in Graph Data",
  booktitle    = "Proceedings of the First SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007)",
  series       = "Lecture Notes in Computer Science",
  volume       = "4890",
  pages        = "153-171",
  month        = "March",
  year         = "2008",
  publisher    = "Springer",
  note         = "This is an extended version of the original workshop paper.",
}

Other publications in the database

» Elena Zheleva
» Lise Getoor