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Annual Privacy Forum

APF 2020: Privacy Technologies and Policy pp 61–72Cite as

Privacy Through Data Recolouring

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12121))

Abstract

Current anonymization techniques for statistical databases exhibit significant limitations, related to the utility-privacy trade-off, the introduction of artefacts, and the vulnerability to correlation. We propose an anonymization technique based on the whitening/recolouring procedure that considers the database as an instance of a random population and applies statistical signal processing methods to it. In response to a query, the technique estimates the covariance matrix of the true data and builds a linear transformation of the data, producing an output that has the same statistical characteristics of the true data up to the second order, but is not directly linked to single records. The technique is applied to a real database containing the location data of taxi trips in New York. We show that the technique reduces the amount of artefacts introduced by noise addition while preserving first- and second-order statistical features of the true data (hence maintaining the utility of the query output).

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Author information

Authors and Affiliations

  1. Italian Data Protection Authority, Piazza Venezia 11, 00187, Rome, Italy

    Giuseppe D’Acquisto

  2. Department of Law, Economics, Politics and Modern Languages, LUMSA University, Via Marcantonio Colonna 19, 00192, Rome, Italy

    Maurizio Naldi

  3. Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy

    Alessandro Mazzoccoli, Fabio Ciminelli & Maurizio Naldi

Authors
  1. Giuseppe D’Acquisto
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  2. Alessandro Mazzoccoli
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  3. Fabio Ciminelli
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  4. Maurizio Naldi
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Corresponding author

Correspondence to Maurizio Naldi .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Luís Antunes

  2. LUMSA University, Rome, Italy

    Maurizio Naldi

  3. LUISS, Rome, Italy

    Giuseppe F. Italiano

  4. Goethe University Frankfurt, Frankfurt am Main, Germany

    Kai Rannenberg

  5. ENISA, Athens, Greece

    Prokopios Drogkaris

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D’Acquisto, G., Mazzoccoli, A., Ciminelli, F., Naldi, M. (2020). Privacy Through Data Recolouring. In: Antunes, L., Naldi, M., Italiano, G., Rannenberg, K., Drogkaris, P. (eds) Privacy Technologies and Policy. APF 2020. Lecture Notes in Computer Science(), vol 12121. Springer, Cham. https://doi.org/10.1007/978-3-030-55196-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-55196-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55195-7

  • Online ISBN: 978-3-030-55196-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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