Privacy-enhancing personalized web search

ABSTRACT

Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user.s interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.

REFERENCES

• 1: James Pitkow , Hinrich Schütze , Todd Cass , Rob Cooley , Don Turnbull , Andy Edmonds , Eytan Adar , Thomas Breuel, Personalized search, Communications of the ACM, v.45 n.9, September 2002 [doi>10.1145/567498.567526]
• 2: Google personalized search: http://www.google.com/psearch
• 3: Yahoo! My Web 2.0: http://myweb2.search.yahoo.com/
• 4: W. Gasarch. A survey on private information retrieval. The bulletin of the European Association for Theoretical Computer Science (EATCS), 82:72--107, 2004.
• 5: Glen Jeh , Jennifer Widom, Scaling personalized web search, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775191]
• 6: Taher H. Haveliwala, Topic-sensitive PageRank, Proceedings of the 11th international conference on World Wide Web, May 07-11, 2002, Honolulu, Hawaii, USA [doi>10.1145/511446.511513]
• 7: Kazunari Sugiyama , Kenji Hatano , Masatoshi Yoshikawa, Adaptive web search based on user profile constructed without any effort from users, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988764]
• 8: Jaime Teevan , Susan T. Dumais , Eric Horvitz, Personalizing search via automated analysis of interests and activities, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076111]
• 9: Paolo Ferragina , Antonio Gulli, A personalized search engine based on web-snippet hierarchical clustering, Special interest tracks and posters of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1062745.1062760]
• 10: Paul Alexandru Chirita , Wolfgang Nejdl , Raluca Paiu , Christian Kohlschütter, Using ODP metadata to personalize search, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076067]
• 11: Hyoung R. Kim , Philip K. Chan, Learning implicit user interest hierarchy for context in personalization, Proceedings of the 8th international conference on Intelligent user interfaces, January 12-15, 2003, Miami, Florida, USA [doi>10.1145/604045.604064]
• 12: M. Speretta, and S. Gauch, Personalizing search based on user search history. In Proc. of International Conference of Knowledge Management(CIKM), Washington D.C., 2004.
• 13: P. Anick. Using terminological feed back for Web search refinement: a log-based study. In Proc. of the 13th International World Wide Web Conference (WWW), New York, New York, May 2004.
• 14: Kathleen R. McKeown , Noemie Elhadad , Vasileios Hatzivassiloglou, Leveraging a common representation for personalized search and summarization in a medical digital library, Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, May 27-31, 2003, Houston, Texas
• 15: A. Kritikopoulos, and M. Sideri. The compass Filter: Search engine result personalization using web communities. In Proc. of Intelligent Techniques in Web Personalization (ITWP), 2003.
• 16: B. Fung, K. Wang and M. Ester. Hierarchical document clustering using frequent itemsets. In Proc. Of SIAM International Conference on Data Mining, San Francisco, May 2003.
• 17: Ke Wang , Chu Xu , Bing Liu, Clustering transactions using large items, Proceedings of the eighth international conference on Information and knowledge management, p.483-490, November 02-06, 1999, Kansas City, Missouri, United States [doi>10.1145/319950.320054]
• 18: Jian-Tao Sun , Hua-Jun Zeng , Huan Liu , Yuchang Lu , Zheng Chen, CubeSVD: a novel approach to personalized Web search, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060803]
• 19: Rakesh Agrawal , Ramakrishnan Srikant, Privacy-preserving data mining, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.439-450, May 15-18, 2000, Dallas, Texas, United States
• 20: Alexandre Evfimievski , Johannes Gehrke , Ramakrishnan Srikant, Limiting privacy breaches in privacy preserving data mining, Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, p.211-222, June 09-11, 2003, San Diego, California [doi>10.1145/773153.773174]
• 21: Latanya Sweeney, k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, v.10 n.5, p.557-570, October 2002 [doi>10.1142/S0218488502001648]
• 22: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 23: W. Alan. Privacy and Freedom. Atheneum Press, Boston, 1967.
• 24: John M. Carroll , Mary Beth Rosson, Paradox of the active user, Interfacing thought: cognitive aspects of human-computer interaction, MIT Press, Cambridge, MA, 1987
• 25: M. Tribus. Thermostatics and Thermodynamics, D. Van Nostrand, New York, NY, 1961.
• 26: Thomas M. Cover , Joy A. Thomas, Elements of information theory, Wiley-Interscience, New York, NY, 1991
• 27: Jiawei Han, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2005
• 28: Feng Qiu , Junghoo Cho, Automatic identification of user interest for personalized search, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135883]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Algorithms, Security

Keywords:
hierarchical user profile, personalized search, privacy