CEO and CIO perspectives on competitive intelligence
Communications of the ACM
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
ACM SIGIR Forum
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
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
"I know what you did last summer": query logs and user privacy
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A survey of query log privacy-enhancing techniques from a policy perspective
ACM Transactions on the Web (TWEB)
Graphs from Search Engine Queries
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Website privacy preservation for query log publishing
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
User k-anonymity for privacy preserving data mining of query logs
Information Processing and Management: an International Journal
More than modelling and hiding: towards a comprehensive view of Web mining and privacy
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Information Sciences: an International Journal
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We introduce the concern of confidentiality protection of business information for the publication of search engine query logs and derived data. We study business confidentiality, as the protection of nonpublic data from institutions, such as companies and people in the public eye. In particular, we relate this concern to the involuntary exposure of confidential Web site information, and we transfer this problem into the field of privacy-preserving data mining. We characterize the possible adversaries interested in disclosing Web site confidential data and the attack strategies that they could use. These attacks are based on different vulnerabilities found in query log for which we present several anonymization heuristics to prevent them. We perform an experimental evaluation to estimate the remaining utility of the log after the application of our anonymization techniques. Our experimental results show that a query log can be anonymized against these specific attacks while retaining a significant volume of useful data.