ACM SIGIR Forum
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
On privacy preservation against adversarial data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
On anonymizing query logs via token-based hashing
Proceedings of the 16th international conference on World Wide Web
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward privacy in public databases
TCC'05 Proceedings of the Second international conference on Theory of Cryptography
PinKDD'07: privacy, security, and trust in KDD post-workshop report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Tree-Based Microaggregation for the Anonymization of Search Logs
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Mining Query Logs: Turning Search Usage Data into Knowledge
Foundations and Trends in Information Retrieval
Privacy-preserving query log mining for business confidentiality protection
ACM Transactions on the Web (TWEB)
Semantic microaggregation for the anonymization of query logs
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
More than modelling and hiding: towards a comprehensive view of Web mining and privacy
Data Mining and Knowledge Discovery
Aggregate suppression for enterprise search engines
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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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.