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Privacy-preserving topic model for tagging recommender systems

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Abstract

Tagging recommender systems provide users the freedom to explore tags and obtain recommendations. The releasing and sharing of these tagging datasets will accelerate both commercial and research work on recommender systems. However, releasing the original tagging datasets is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive information from tagging datasets with only a little background information. Recently, several privacy techniques have been proposed to address the problem, but most of these lack a strict privacy notion, and rarely prevent individuals being re-identified from the dataset. This paper proposes a privacy- preserving tag release algorithm, PriTop. This algorithm is designed to satisfy differential privacy, a strict privacy notion with the goal of protecting users in a tagging dataset. The proposed PriTop algorithm includes three privacy-preserving operations: Private topic model generation structures the uncontrolled tags; private weight perturbation adds Laplace noise into the weights to hide the numbers of tags; while private tag selection finally finds the most suitable replacement tags for the original tags, so the exact tags can be hidden. We present extensive experimental results on four real-world datasets, Delicious, MovieLens, Last.fm and BibSonomy. While the recommendation algorithm is successful in all the cases, our results further suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy.

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Notes

  1. https://delicious.com/.

  2. http://www.bibsonomy.org/.

  3. http://www.last.fm/.

  4. https://www.netflix.com/.

  5. http://www.dai-labor.de/.

  6. http://www.kde.cs.uni-kassel.de/ws/dc09/.

  7. http://ir.ii.uam.es/hetrec2011.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia

    Tianqing Zhu, Gang Li & Wanlei Zhou

  2. School of Information and Security Engineering, Zhongnan University of Economics and Law, Wuhan, China

    Ping Xiong

  3. School of Information Technology, Wuhan Polytechnic University, Wuhan, China

    Cao Yuan

Authors
  1. Tianqing Zhu
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  2. Gang Li
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  3. Wanlei Zhou
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  4. Ping Xiong
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  5. Cao Yuan
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Corresponding author

Correspondence to Gang Li.

Additional information

This manuscript is an extended version of PAKDD best student paper.

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Zhu, T., Li, G., Zhou, W. et al. Privacy-preserving topic model for tagging recommender systems. Knowl Inf Syst 46, 33–58 (2016). https://doi.org/10.1007/s10115-015-0832-9

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  • DOI: https://doi.org/10.1007/s10115-015-0832-9

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