Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Usability of anonymous web browsing: an examination of Tor interfaces and deployability
Proceedings of the 3rd symposium on Usable privacy and security
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Hiding sensitive knowledge without side effects
Knowledge and Information Systems
Privacy in Web Search Query Log Mining
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
A Scalable Peer-to-Peer Group Communication Protocol
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Embellishing text search queries to protect user privacy
Proceedings of the VLDB Endowment
Stock fraud detection using peer group analysis
Expert Systems with Applications: An International Journal
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Privacy protection in web search engines is becoming more and more serious in recent days. In this paper, we study the problem of privacy protection in web search, with a special focus on IP-address based personalized web search. Our goal is to break the linkage between users' identities (e.g., IP address) and their issued queries so as to prevent privacy breaches. Our privacy model, which shares similar characteristics of l-diversity in privacy preserving data publishing of relational data, provides a strong privacy guarantee in web search. The central idea of our privacy model is to protect user's search activities within a social peer group. A social peer group contains a set of individual users. From search engines's perspective, search queries issued by users from the same peer group cannot be uniquely linked to individuals within the group. A framework based on grouping social peer users is proposed to achieve the privacy requirement. We also provide some experimental results to show that our methods achieve high efficiency in practice.