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Privacy-preserving LOF outlier detection

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Abstract

LOF is a well-known approach for density-based outlier detection and has received much attention recently. It is important to design a privacy-preserving LOF outlier detection algorithm as the data on which LOF runs is typically spilt among multiple participants and no one is willing to disclose his sensitive information due to legal or moral considerations. This is, however, a hard problem since participants need to find the maximum one of the distances between an object and its k-Nearest Neighbors (k-NN) without learning the information of these objects. In this paper, we propose an efficient protocol for privacy-preserving LOF outlier detection. We first employ a shuffle protocol to permute the distance vectors owned by different participants. Then, we design a secure selection method to obtain the garbled k-NN indexes and shares of k-distance for given objects. For each object, we make use of the k-distance of all objects to construct a vector, based on which the permute protocol is executed again to obtain new shares of k-distance. Finally, the shares corresponding to the garbled k-NN indexes are selected as the expected result. Our protocol ensures that all the intermediates are shared between multiple participants and thus avoid information leaking. In addition, our protocol is efficient as we prove that the computation and communication complexity of our protocol is bounded by \(O(n^2)\).

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Notes

  1. http://viff.dk/.

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Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions. This work was supported by the National Natural Science Foundation of China (Nos. 60903217 & 61202407 & 61003044), the Fundamental Research Funds for the Central Universities (Nos. WK0110000027 & WK0110000033), the Guangdong Province Strategic Cooperation Project with the Chinese Academy of Sciences (No. 2012B090400013) and the Natural Science Foundation of Jiangsu Province of China (No. BK2011357).

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Lu Li, Liusheng Huang, Wei Yang, Xiaohui Yao & An Liu

  2. Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, China

    Lu Li, Liusheng Huang, Wei Yang & An Liu

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  1. Lu Li
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  2. Liusheng Huang
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  3. Wei Yang
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  4. Xiaohui Yao
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  5. An Liu
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Correspondence to Lu Li.

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Li, L., Huang, L., Yang, W. et al. Privacy-preserving LOF outlier detection. Knowl Inf Syst 42, 579–597 (2015). https://doi.org/10.1007/s10115-013-0692-0

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