Abstract
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.
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References
AOL research website, no longer online, http://research.aol.com
Arrington, M.: AOL proudly releases massive amounts of private data (2006), http://www.techcrunch.com/2006/08/06/aol-proudly-releases-massive-amounts-of-user-search-data/
Barbaro, M., Zeller, T.: A face is exposed for AOL searcher no. 4417749, New York Times (2006)
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)
Adar, E.: User 4xxxxx9: Anonymizing query logs. In: Query Log Analysis: Social and Technological Challenges, Workshop in WWW 2007 (2007)
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)
Chawla, S., Dwork, C., McSherry, F., Smith, A., Wee, H.: Toward privacy in public databases. In: Theory of Cryptography Conference, pp. 363–385 (2005)
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)
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)
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical report (1998)
Broder, A.: A taxonomy of web search. ACM SIGIR Forum 36(2), 3–10 (2002)
Albert, R., Jeong, H., Barabasi, A.L.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)
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)
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Poblete, B., Spiliopoulou, M., Baeza-Yates, R. (2008). Website Privacy Preservation for Query Log Publishing. In: Bonchi, F., Ferrari, E., Malin, B., Saygin, Y. (eds) Privacy, Security, and Trust in KDD. PInKDD 2007. Lecture Notes in Computer Science, vol 4890. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78478-4_5
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DOI: https://doi.org/10.1007/978-3-540-78478-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78477-7
Online ISBN: 978-3-540-78478-4
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