iReduct: differential privacy with reduced relative errors
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
PrivBasis: frequent itemset mining with differential privacy
Proceedings of the VLDB Endowment
Functional mechanism: regression analysis under differential privacy
Proceedings of the VLDB Endowment
Non-interactive differential privacy: a survey
Proceedings of the First International Workshop on Open Data
Semantic search log k-anonymization with generalized k-cores of query concept graph
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Practical differential privacy via grouping and smoothing
Proceedings of the VLDB Endowment
Efficient Time-Stamped Event Sequence Anonymization
ACM Transactions on the Web (TWEB)
Differentially private histogram publication
The VLDB Journal — The International Journal on Very Large Data Bases
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Search engine companies collect the “database of intentions,” the histories of their users' search queries. These search logs are a gold mine for researchers. Search engine companies, however, are wary of publishing search logs in order not to disclose sensitive information. In this paper, we analyze algorithms for publishing frequent keywords, queries, and clicks of a search log. We first show how methods that achieve variants of k-anonymity are vulnerable to active attacks. We then demonstrate that the stronger guarantee ensured by ε-differential privacy unfortunately does not provide any utility for this problem. We then propose an algorithm ZEALOUS and show how to set its parameters to achieve (ε,δ )-probabilistic privacy. We also contrast our analysis of ZEALOUS with an analysis by Korolova et al. [17] that achieves (ε′,δ′)-indistinguishability. Our paper concludes with a large experimental study using real applications where we compare ZEALOUS and previous work that achieves k-anonymity in search log publishing. Our results show that ZEALOUS yields comparable utility to k-anonymity while at the same time achieving much stronger privacy guarantees.