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Integral Privacy

Part of the Lecture Notes in Computer Science book series (LNSC,volume 10052)

Abstract

When considering data provenance some problems arise from the need to safely handle provenance related functionality. If some modifications have to be performed in a data set due to provenance related requirements, e.g. remove data from a given user or source, this will affect not only the data itself but also all related models and aggregated information obtained from the data. This is specially aggravated when the data are protected using a privacy method (e.g. masking method), since modification in the data and the model can leak information originally protected by the privacy method. To be able to evaluate privacy related problems in data provenance we introduce the notion of integral privacy as compared to the well known definition of differential privacy.

Keywords

  • Decision Tree
  • Data Privacy
  • Voronoi Tesselation
  • Differential Privacy
  • Disclosure Risk

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgments

Partial support by the Spanish MINECO (project TIN2014-55243-P) and Catalan AGAUR (2014-SGR-691) is acknowledged.

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Correspondence to Vicenç Torra .

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Torra, V., Navarro-Arribas, G. (2016). Integral Privacy. In: Foresti, S., Persiano, G. (eds) Cryptology and Network Security. CANS 2016. Lecture Notes in Computer Science(), vol 10052. Springer, Cham. https://doi.org/10.1007/978-3-319-48965-0_44

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  • DOI: https://doi.org/10.1007/978-3-319-48965-0_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48964-3

  • Online ISBN: 978-3-319-48965-0

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