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Privacy-Preserving Statistical Data Analysis on Federated Databases

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Privacy Technologies and Policy (APF 2014)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 8450))

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Abstract

The quality of empirical statistical studies is tightly related to the quality and amount of source data available. However, it is often hard to collect data from several sources due to privacy requirements or a lack of trust. In this paper, we propose a novel way to combine secure multi-party computation technology with federated database systems to preserve privacy in statistical studies that combine and analyse data from multiple databases. We describe an implementation on two real-world platforms—the Sharemind secure multi-party computation and the X-Road database federation platform. Our solution enables the privacy-preserving linking and analysis of databases belonging to different institutions. Indeed, a preliminary analysis from the Estonian Data Protection Inspectorate suggests that the correct implementation of our solution ensures that no personally identifiable information is processed in such studies. Therefore, our proposed solution can potentially reduce the costs of conducting statistical studies on shared data.

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Author information

Authors and Affiliations

  1. Cybernetica, Mäealuse 2/1, 12618, Tallinn, Estonia

    Dan Bogdanov, Liina Kamm, Riivo Talviste & Jan Willemson

  2. Institute of Computer Science, University of Tartu, Liivi 2, 50409, Tartu, Estonia

    Liina Kamm, Sven Laur & Riivo Talviste

  3. Institute of Journalism, Communication and Information Studies, University of Tartu, Lossi 36, 51003, Tartu, Estonia

    Pille Pruulmann-Vengerfeldt

  4. ELIKO Competence Centre in Electronics-, Info- and Communication Technologies, Mäealuse 2/1, Tallinn, Estonia

    Jan Willemson

Authors
  1. Dan Bogdanov
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  2. Liina Kamm
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  3. Sven Laur
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  4. Pille Pruulmann-Vengerfeldt
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  5. Riivo Talviste
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  6. Jan Willemson
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering (ESAT), KU Leuven and iMinds, Kasteelpark Arenberg 10, Bus 2446, 3001, Leuven, Belgium

    Bart Preneel

  2. Information Security and Data Protection Unit, ENISA, 1 Vasilissis Sofias, Marousi, 15124, Athens, Greece

    Demosthenes Ikonomou

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Bogdanov, D., Kamm, L., Laur, S., Pruulmann-Vengerfeldt, P., Talviste, R., Willemson, J. (2014). Privacy-Preserving Statistical Data Analysis on Federated Databases. In: Preneel, B., Ikonomou, D. (eds) Privacy Technologies and Policy. APF 2014. Lecture Notes in Computer Science, vol 8450. Springer, Cham. https://doi.org/10.1007/978-3-319-06749-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-06749-0_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06748-3

  • Online ISBN: 978-3-319-06749-0

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