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Guided perturbation: towards private and accurate mining

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Abstract

There have been two methods for privacy- preserving data mining: the perturbation approach and the cryptographic approach. The perturbation approach is typically very efficient, but it suffers from a tradeoff between accuracy and privacy. In contrast, the cryptographic approach usually maintains accuracy, but it is more expensive in computation and communication overhead. We propose a novel perturbation method, called guided perturbation. Specifically, we focus on a central problem of privacy-preserving data mining—the secure scalar product problem of vertically partitioned data, and give a solution based on guided perturbation, with good, provable privacy guarantee. Our solution achieves accuracy comparable to the cryptographic solutions, while keeping the efficiency of perturbation solutions. Our experimental results show that it can be more than one hundred times faster than a typical cryptographic solution.

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Authors and Affiliations

  1. Computer Science and Engineering Department, State University of New York at Buffalo, Amherst, NY, 14260, USA

    Sheng Zhong

  2. Imagine Software, Inc., 233 Broadway, 17th Fl, New York, NY, 10279, USA

    Zhiqiang Yang

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  1. Sheng Zhong
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  2. Zhiqiang Yang
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Correspondence to Sheng Zhong.

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Zhong, S., Yang, Z. Guided perturbation: towards private and accurate mining. The VLDB Journal 17, 1165–1177 (2008). https://doi.org/10.1007/s00778-007-0056-z

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