De-anonymising Social Network Posts by Linking with Résumé

  • Yohei OgawaEmail author
  • Eina Hashimoto
  • Masatsugu Ichino
  • Isao Echizen
  • Hiroshi Yoshiura
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 228)


We have developed a system for identifying the person who posted posts of interest. It calculates the similarity between the posts of interest and the résumé of each candidate person and then identifies the résumé with the highest similarity as that of the posting person. Identification accuracy was improved by using the posts of persons other than the target person. Evaluation using 30 student volunteers who permitted the use of their résumés and sets of tweets showed that using information from tweets of other persons dramatically improved identification accuracy. Identification accuracy was 0.36 and 0.53 when the number of other persons was 4 and 9, respectively. Those that the target person can be limited in 10 % of the candidates were 0.72 both with 4 and 9 such employees.


Social network Privacy Security Anonymity 



This work was supported by ISPS KAKENHI Grant Number 26330153.


  1. 1.
    Okuno, T., Ichino, M., Echizen, I., Utsumi, A., Yoshiura, H.: Ineluctable background checking on social networks: linking job seeker’s résumé and posts. In: 5th IEEE International Workshop on Security and Social Networking (SESOC 2013) (2013)Google Scholar
  2. 2.
    Accorsi, R., Sato, Y., Kai, S.: Compliance monitor for early warning risk determination. Wirtschaftsinformatik 50(5), 375–382 (2008). Vieweg VerlagCrossRefGoogle Scholar
  3. 3.
    Wohlgemuth, S.: Resilience as a new enforcement model for IT security based on usage control. In: 5th Security & Privacy Workshop on Data Usage Management, 35th IEEE Symposium on Security and Privacy (S&P) 2014, pp. 31–38 (2014)Google Scholar
  4. 4.
    Fung, B., Wang, K., Fu, A., Yu, P.: Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques. Chapman and Hall/CRC, Boca Raton (2010)CrossRefGoogle Scholar
  5. 5.
    Sweeney, L.: k-anonymity: a Model for Protecting Privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002)CrossRefGoogle Scholar
  6. 6.
    Machanavajjhala, A., Gehrke, J., Kifer, D.: ℓ-diversity: privacy beyond k-anonymity. In: 22nd IEEE International Conference on Data Engineering, pp. 24–75 (2006)Google Scholar
  7. 7.
    Soria-Comas, J., et al.: Probabilistic k-anonymity through microaggregation and data swapping. In: IEEE International Conference on Fuzzy Systems, pp. 1–8 (2012)Google Scholar
  8. 8.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Shokri, R., et al.: Unraveling an old cloak: k-anonymity for location privacy. In: 9th Annual ACM Workshop on Privacy in the Electronic Society, pp. 115–118 (2010)Google Scholar
  10. 10.
    Nguyen-Son, H.-Q., Tran, M.-T., Tien, D.T., Yoshiura, H., Sonehara, N., Echizen, I.: Automatic anonymous fingerprinting of text posted on social networking services. In: Shi, Y.Q., Kim, H.-J., Pérez-González, F. (eds.) IWDW 2012. LNCS, vol. 7809, pp. 410–424. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Winkler, W.E.: Masking and re-identification methods for public-use microdata: overview and research problems. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 231–246. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: 29th IEEE Security & Privacy, pp. 111–125 (2008)Google Scholar
  13. 13.
    Watanabe, N., Yoshiura, H.: Detecting revelation of private information on online social networks. In: 6th IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 502–505 (2010)Google Scholar
  14. 14.
    Polakis, I., et al.: Using social networks to harvest email addresses. In: 9th Annual ACM Workshop on Privacy in the Electronic Society, pp. 11–20 (2010)Google Scholar
  15. 15.
    Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefGoogle Scholar
  16. 16.
    Jones, K.: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28(1), 11–21 (1972)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yohei Ogawa
    • 1
    Email author
  • Eina Hashimoto
    • 1
  • Masatsugu Ichino
    • 1
  • Isao Echizen
    • 2
  • Hiroshi Yoshiura
    • 1
  1. 1.University of Electro-CommunicationsChofu, TokyoJapan
  2. 2.National Institute of InformaticsChiyodaku, TokyoJapan

Personalised recommendations