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International Conference on Cryptology and Network Security

CANS 2016: Cryptology and Network Security pp 661–669Cite as

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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  • DOI: 10.1007/978-3-319-48965-0_44
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References

  1. Barbier, G., Feng, Z., Gundecha, P., Liu, H.: Provenance Data in Social Media. Morgan & Claypool Publishers, San Rafael (2013)

    Google Scholar 

  2. Bertino, E., Ghinita, G., Kantarcioglu, M., Nguyen, D., Park, J., Sandhu, R., Sultana, S., Thuraisingham, B., Xu, S.: A roadmap for privacy-enhanced secure data provenance. J. Intell. Inf. Syst. 43, 481–501 (2014)

    CrossRef  Google Scholar 

  3. Buneman, P., Khanna, S., Wang-Chiew, T.: A characterization of data provenance. In: International Conference on Database Theory, pp. 316–330. Springer

    Google Scholar 

  4. Das, S.: Functional Fractional Calculus. Springer, Dordrecht (2008)

    MATH  Google Scholar 

  5. Domingo-Ferrer, J., Torra, V.: A quantitative comparison of disclosure control methods for microdata. In: Doyle, P., Lane, J.I., Theeuwes, J.J.M., Zayatz, L., (eds.) Confidentiality, Disclosure, Data Access: Theory and Practical Applications for Statistical Agencies, North-Holland, pp. 111–134 (2001)

    Google Scholar 

  6. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  7. Hasan, R., Sion, R., Winslett, M.: (2007) Introducing secure provenance: problems and challenges. In: Proceedings StorageSST. ACM, New York (2007)

    Google Scholar 

  8. Muralidhar, K., Sarathy, R.: Generating sufficiency-based non-synthetic perturbed data. Trans. Data Priv. 1(1), 17–33 (2008)

    MathSciNet  Google Scholar 

  9. Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-science. ACM Sigmod Rec. 34(3), 31–36 (2005)

    CrossRef  Google Scholar 

  10. Torra, V., Navarro-Arribas, G.: Data Privacy, WIREs Data Mining and Knowledge Discovery, 4(4), 269–280 (2014)

    Google Scholar 

  11. Winkler, W.E.: Re-identification methods for masked microdata. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 216–230. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

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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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Authors and Affiliations

  1. School of Informatics, University of Skövde, Skövde, Sweden

    Vicenç Torra

  2. Department of Information and Communication Engineering, Universitat Autònoma de Barcelona, Catalonia, Spain

    Guillermo Navarro-Arribas

Authors
  1. Vicenç Torra
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  2. Guillermo Navarro-Arribas
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Corresponding author

Correspondence to Vicenç Torra .

Editor information

Editors and Affiliations

  1. Università degli Studi di Milano, Crema, Italy

    Sara Foresti

  2. Università degli Studi di Salerno, Fisciano, Italy

    Giuseppe Persiano

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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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