Website Privacy Preservation for Query Log Publishing

  • Barbara Poblete
  • Myra Spiliopoulou
  • Ricardo Baeza-Yates
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4890)


In this paper we study privacy preservation for the publication of search engine query logs. We introduce a new privacy concern, website privacy as a special case of business privacy. We define the possible adversaries who could be interested in disclosing website information and the vulnerabilities in the query log, which they could exploit. We elaborate on anonymization techniques to protect website information, discuss different types of attacks that an adversary could use and propose an anonymization strategy for one of these attacks. We then present a graph-based heuristic to validate the effectiveness of our anonymization method and perform an experimental evaluation of this approach. Our experimental results show that the query log can be appropriately anonymized against the specific attack, while retaining a significant volume of useful data.


Search Engine Privacy Preservation Privacy Preserve User Privacy Privacy Breach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    AOL research website, no longer online,
  2. 2.
    Arrington, M.: AOL proudly releases massive amounts of private data (2006),
  3. 3.
    Barbaro, M., Zeller, T.: A face is exposed for AOL searcher no. 4417749, New York Times (2006)Google Scholar
  4. 4.
    Kumar, R., Novak, J., Pang, B., Tomkins, A.: On anonymizing query logs via token-based hashing. In: WWW 2007: Proceedings of the 16th international conference on World Wide Web, pp. 629–638. ACM Press, New York (2007)CrossRefGoogle Scholar
  5. 5.
    Adar, E.: User 4xxxxx9: Anonymizing query logs. In: Query Log Analysis: Social and Technological Challenges, Workshop in WWW 2007 (2007)Google Scholar
  6. 6.
    Verykios, V., Bertino, E., Fovino, I., Provenza, L., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining. SIGMOD Record 33(1), 50–57 (2004)CrossRefGoogle Scholar
  7. 7.
    Chawla, S., Dwork, C., McSherry, F., Smith, A., Wee, H.: Toward privacy in public databases. In: Theory of Cryptography Conference, pp. 363–385 (2005)Google Scholar
  8. 8.
    Kifer, D., Gehrke, J.: Injecting utility into anonymized datasets. In: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp. 217–228 (2006)Google Scholar
  9. 9.
    Aggarwal, C., Pei, J., Zhang, B.: On privacy preservation against adversarial data mining. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 510–516 (2006)Google Scholar
  10. 10.
    Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical report (1998)Google Scholar
  11. 11.
    Broder, A.: A taxonomy of web search. ACM SIGIR Forum 36(2), 3–10 (2002)CrossRefGoogle Scholar
  12. 12.
    Albert, R., Jeong, H., Barabasi, A.L.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)CrossRefGoogle Scholar
  13. 13.
    Baeza-Yates, R., Tiberi, A.: Extracting semantic relations from query logs. In: ACM SIGKDD international conference on Knowledge discovery and data mining (to appear, 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Barbara Poblete
    • 1
  • Myra Spiliopoulou
    • 2
  • Ricardo Baeza-Yates
    • 1
    • 3
  1. 1.Web Research GroupUniversity Pompeu FabraBarcelonaSpain
  2. 2.Otto-von-Guericke-University MagdeburgGermany
  3. 3.Yahoo! ResearchBarcelonaSpain

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