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Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework

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Privacy, Security, and Trust in KDD (PInKDD 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4890))

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Abstract

As more and more person-specific data like health information becomes available, increasing attention is paid to confidentiality and privacy protection. One proposed model of privacy protection is k-Anonymity, where a dataset is k-anonymous if each record is identical to at least (k-1) others in the dataset. Our goal is to minimize information loss while transforming a collection of records to satisfy the k-Anonymity model. The downside to current greedy anonymization algorithms is their potential to get stuck at poor local optimums. In this paper, we propose an Ordered Greed Framework for k-Anonymity. Using our framework, designers can avoid the poor-local-optimum problem by adding stochastic elements to their greedy algorithms. Our preliminary experimental results indicate improvements in both runtime and solution quality. We also discover a surprising result concerning at least two widely-accepted greedy optimization algorithms in the literature. More specifically, for anonymization algorithms that process datasets in column-wise order, we show that a random column ordering can lead to significantly higher quality solutions than orderings determined by known greedy heuristics.

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Authors and Affiliations

  1. Dept. of Computer Science, Memorial University, St. John’s, NL, Canada

    Rhonda Chaytor

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  1. Rhonda Chaytor
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Editor information

Francesco Bonchi Elena Ferrari Bradley Malin YĂĽcel Saygin

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Chaytor, R. (2008). Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework. In: Bonchi, F., Ferrari, E., Malin, B., Saygin, Y. (eds) Privacy, Security, and Trust in KDD. PInKDD 2007. Lecture Notes in Computer Science, vol 4890. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78478-4_3

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  • DOI: https://doi.org/10.1007/978-3-540-78478-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78477-7

  • Online ISBN: 978-3-540-78478-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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