Publishing search logs—a comparative study of privacy guarantees

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 a...

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Main Authors: Gӧtz, Michaela, Machanavajjhala, Ashwin, Wang, Guozhang, Xiao, Xiaokui, Gehrke, Johannes
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/99136
http://hdl.handle.net/10220/13479
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-991362020-05-28T07:18:34Z Publishing search logs—a comparative study of privacy guarantees Gӧtz, Michaela Machanavajjhala, Ashwin Wang, Guozhang Xiao, Xiaokui Gehrke, Johannes School of Computer Engineering DRNTU::Engineering::Computer science and engineering 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. 2013-09-16T06:36:03Z 2019-12-06T20:03:45Z 2013-09-16T06:36:03Z 2019-12-06T20:03:45Z 2012 2012 Journal Article Gӧtz, M., Machanavajjhala, A., Wang, G., Xiao, X., & Gehrke, J. (2012). Publishing Search Logs—A Comparative Study of Privacy Guarantees. IEEE Transactions on Knowledge and Data Engineering, 24(3), 520-532. 1041-4347 https://hdl.handle.net/10356/99136 http://hdl.handle.net/10220/13479 10.1109/TKDE.2011.26 en IEEE transactions on knowledge and data engineering © 2012 IEEE
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Gӧtz, Michaela
Machanavajjhala, Ashwin
Wang, Guozhang
Xiao, Xiaokui
Gehrke, Johannes
Publishing search logs—a comparative study of privacy guarantees
description 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.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Gӧtz, Michaela
Machanavajjhala, Ashwin
Wang, Guozhang
Xiao, Xiaokui
Gehrke, Johannes
format Article
author Gӧtz, Michaela
Machanavajjhala, Ashwin
Wang, Guozhang
Xiao, Xiaokui
Gehrke, Johannes
author_sort Gӧtz, Michaela
title Publishing search logs—a comparative study of privacy guarantees
title_short Publishing search logs—a comparative study of privacy guarantees
title_full Publishing search logs—a comparative study of privacy guarantees
title_fullStr Publishing search logs—a comparative study of privacy guarantees
title_full_unstemmed Publishing search logs—a comparative study of privacy guarantees
title_sort publishing search logs—a comparative study of privacy guarantees
publishDate 2013
url https://hdl.handle.net/10356/99136
http://hdl.handle.net/10220/13479
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