k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A temporal comparison of AltaVista Web searching: Research Articles
Journal of the American Society for Information Science and Technology
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
An implementation of the FP-growth algorithm
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
t-Plausibility: Generalizing Words to Desensitize Text
Transactions on Data Privacy
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The revenue of search-engine providers strongly depends on targeted advertisement. Targeted advertisement is becoming more reliant on personal data. This puts user privacy at risk. One way to improve privacy is to anonymize search logs, but this reduces usefulness for ad placement. Further, the usefulness depends on the target function used for the anonymization. This paper is the first to study this tradeoff systematically. We quantify the usefulness of an anonymized search log for advertisement purposes, by estimating outcomes such as the number of clicks on ads or the number of ad impressions possible after anonymization. A main result is that anonymized search logs are still useful for advertisement purposes, but the extent strongly depends on the target function.