Entry Ji:2016:GGD from tissec.bib

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BibTeX entry

@Article{Ji:2016:GGD,
  author =       "Shouling Ji and Weiqing Li and Mudhakar Srivatsa and
                 Jing Selena He and Raheem Beyah",
  title =        "General Graph Data De-Anonymization: From Mobility
                 Traces to Social Networks",
  journal =      j-TISSEC,
  volume =       "18",
  number =       "4",
  pages =        "12:1--12:??",
  month =        may,
  year =         "2016",
  CODEN =        "ATISBQ",
  DOI =          "https://doi.org/10.1145/2894760",
  ISSN =         "1094-9224 (print), 1557-7406 (electronic)",
  ISSN-L =       "1094-9224",
  bibdate =      "Sat May 21 08:19:26 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tissec.bib",
  abstract =     "When people utilize social applications and services,
                 their privacy suffers a potential serious threat. In
                 this article, we present a novel, robust, and effective
                 de-anonymization attack to mobility trace data and
                 social data. First, we design a Unified Similarity (US)
                 measurement, which takes account of local and global
                 structural characteristics of data, information
                 obtained from auxiliary data, and knowledge inherited
                 from ongoing de-anonymization results. By analyzing the
                 measurement on real datasets, we find that some data
                 can potentially be de-anonymized accurately and the
                 other can be de-anonymized in a coarse granularity.
                 Utilizing this property, we present a US-based
                 De-Anonymization (DA) framework, which iteratively
                 de-anonymizes data with accuracy guarantee. Then, to
                 de-anonymize large-scale data without knowledge of the
                 overlap size between the anonymized data and the
                 auxiliary data, we generalize DA to an Adaptive
                 De-Anonymization (ADA) framework. By smartly working on
                 two core matching subgraphs, ADA achieves high
                 de-anonymization accuracy and reduces computational
                 overhead. Finally, we examine the presented
                 de-anonymization attack on three well-known mobility
                 traces: St Andrews, Infocom06, and Smallblue, and three
                 social datasets: ArnetMiner, Google+, and Facebook. The
                 experimental results demonstrate that the presented
                 de-anonymization framework is very effective and robust
                 to noise. The source code and employed datasets are now
                 publicly available at SecGraph [2015].",
  acknowledgement = ack-nhfb,
  articleno =    "12",
  fjournal =     "ACM Transactions on Information and System Security",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J789",
}

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