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An Ad Omnia Approach to Defining and Achieving Private Data Analysis

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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

We briefly survey several privacy compromises in published datasets, some historical and some on paper. An inspection of these suggests that the problem lies with the nature of the privacy-motivated promises in question. These are typically syntactic, rather than semantic. They are also ad hoc , with insufficient argument that fulfilling these syntactic and ad hoc conditions yields anything like what most people would regard as privacy. We examine two comprehensive, or ad omnia, guarantees for privacy in statistical databases discussed in the literature, note that one is unachievable, and describe implementations of the other.

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Authors and Affiliations

  1. Microsoft Research,  

    Cynthia Dwork

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  1. Cynthia Dwork
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Francesco Bonchi Elena Ferrari Bradley Malin Yücel Saygin

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Dwork, C. (2008). An Ad Omnia Approach to Defining and Achieving Private Data Analysis. 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_1

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  • DOI: https://doi.org/10.1007/978-3-540-78478-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78477-7

  • Online ISBN: 978-3-540-78478-4

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