Structure Based Data De-Anonymization of Social Networks and Mobility Traces

  • Shouling Ji
  • Weiqing Li
  • Mudhakar Srivatsa
  • Jing Selena He
  • Raheem Beyah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8783)


We present a novel de-anonymization attack on mobility trace data and social data. First, we design an Unified Similarity (US) measurement, based on which we present a US based De-Anonymization (DA) framework which iteratively de-anonymizes data with an accuracy guarantee. Then, to de-anonymize data without the knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. Finally, we examine DA/ADA on mobility traces and social data sets.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography. In: WWW 2007 (2007)Google Scholar
  2. 2.
    Narayanan, A., Shmatikov, V.: De-anonymizing Social Networks. In: S&P 2009 (2009)Google Scholar
  3. 3.
    Srivatsa, M., Hicks, M.: Deanonymizing Mobility Traces: Using Social Networks as a Side-Channel. In: CCS 2012 (2012)Google Scholar
  4. 4.
    Narayanan, A., Shmatikov, V.: Robust De-anonymization of Large Sparse Datasets (De-anonymizing the Netflix Prize Dataset). In: S&P 2008 (2008)Google Scholar
  5. 5.
    Goodin, D.: Poorly anonymized logs reveal NYC cab drivers detailed whereabouts,
  6. 6.
    Singh, K., Bhola, S., Lee, W.: xBook: Redesigning Privacy Control in Social Networking Platforms. In: USENIX 2009 (2009)Google Scholar
  7. 7.
    Hornyack, P., Han, S., Jung, J., Schechter, S., Wetherall, D.: “These Aren’t the Droids You’re Looking For”: Retrofitting Android to Protect Data from Imperious Applications. In: CCS 2011 (2011)Google Scholar
  8. 8.
    Egele, M., Kruegel, C., Kirda, E., Vigna, G.: PiOS: Detecting Privacy Leaks in iOS Applications. In: NDSS 2011 (2011)Google Scholar
  9. 9.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths. Social Networks 32, 245–251 (2010)CrossRefGoogle Scholar
  10. 10.
    Gong, N.Z., Talwalkar, A., Mackey, L., Huang, L., Shin, E.C.R., Stefanov, E., Shi, E., Song, D.: Jointly Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN). In: SNA-KDD 2012 (2012)Google Scholar
  11. 11.
    Bigwood, G., Rehunathan, D., Bateman, M., Henderson, T., Bhatti, S.: CRAWDAD data set st_andrews/sassy (v. 2011-06-03) (June 2011), Downloaded from
  12. 12.
  13. 13.
    Scott, J., Gass, R., Crowcroft, J., Hui, P., Diot, C., Chaintreau, A.: CRAWDAD data set cambridge/haggle (v. 2009-05-29) (May 2009), Downloaded from
  14. 14.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: Extraction and Mining of Academic Social Networks. In: KDD 2008 (2008)Google Scholar
  15. 15.
    Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the Evolution of User Interaction in Facebook. In: WOSN 2009 (2009)Google Scholar
  16. 16.
    Pham, H., Shahabi, C., Liu, Y.: EBM - An Entropy-Based Model to Infer Social Strength from Spatiotemporal Data. In: Sigmod 2013 (2013)Google Scholar
  17. 17.
    Ji, S., Li, W., Srivatsa, M., He, J., Beyah, R.: Technical Report: Data De-anonymization: From Mobility Traces to On-line Social Networks,

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shouling Ji
    • 1
  • Weiqing Li
    • 1
  • Mudhakar Srivatsa
    • 2
  • Jing Selena He
    • 3
  • Raheem Beyah
    • 1
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.IBM T.J. Watson Research CenterYorktown HeightsUSA
  3. 3.KSUKennesawUSA

Personalised recommendations