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International Conference on Financial Cryptography and Data Security

FC 2015: Financial Cryptography and Data Security pp 427–436Cite as

A Short Paper on the Incentives to Share Private Information for Population Estimates

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 8975))

Abstract

Consumers are often willing to contribute their personal data for analytics projects that may create new insights into societal problems. However, consumers also have justified privacy concerns about the release of their data.

We study the trade-off between privacy concerns related to data release and the incentives to contribute to the estimation of a population average of a private attribute. Consumers may decide whether to participate in the analytics project, and what level of data precision they are willing to provide. We show that setting a minimum precision level for participating users leads to a strict improvement of the estimation.

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Acknowledgements

This work was funded by the French Government (National Research Agency, ANR) through the “Investments for the Future” Program reference # ANR-11-LABX-0031-01. We would like to thank the anonymous reviewers and Alvaro Cardenas for their helpful comments.

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Authors and Affiliations

  1. EURECOM, Biot, France

    Michela Chessa & Patrick Loiseau

  2. The Pennsylvania State University, State College, USA

    Jens Grossklags

Authors
  1. Michela Chessa
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  2. Jens Grossklags
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  3. Patrick Loiseau
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Corresponding author

Correspondence to Michela Chessa .

Editor information

Editors and Affiliations

  1. University of Innsbruck, Innsbruck, Austria

    Rainer Böhme

  2. NTT Laboratories, Tokyo, Japan

    Tatsuaki Okamoto

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Chessa, M., Grossklags, J., Loiseau, P. (2015). A Short Paper on the Incentives to Share Private Information for Population Estimates. In: Böhme, R., Okamoto, T. (eds) Financial Cryptography and Data Security. FC 2015. Lecture Notes in Computer Science(), vol 8975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47854-7_25

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  • DOI: https://doi.org/10.1007/978-3-662-47854-7_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47853-0

  • Online ISBN: 978-3-662-47854-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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