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Valuation of Differential Privacy Budget in Data Trade: A Conjoint Analysis

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Privacy and Identity Management (Privacy and Identity 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 671))

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Abstract

Differential privacy has been proposed as a rigorous privacy guarantee for computation mechanisms. However, it is still unclear how data collectors can correctly and intuitively configure the value of the privacy budget parameter \(\varepsilon \) for differential privacy, such that the privacy of involved individuals is protected. In this work, we seek to investigate the trade-offs between differential privacy valuation, scenario properties, and preferred differential privacy level of individuals in a data trade. Using a choice-based conjoint analysis (\(N = 139)\), we mimic the decision-making process of individuals under different data-sharing scenarios. We found that, as hypothesized, individuals required lower payments from a data collector for sharing their data, as more substantial perturbation was applied as part of a differentially private data analysis. Furthermore, respondents selected scenarios with lower \(\varepsilon \) values (requiring more privacy) for indefinitely-retained data for profit generation than for temporarily-retained data with a non-commercial purpose. Our findings may help data processors better tune the differential privacy budget for their data analysis based on individual privacy valuation and contextual properties.

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Notes

  1. 1.

    Full survey protocol and evaluation code are available at https://github.com/iWitLab/valuating_differential_privacy_budget.

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Acknowledgments

This work was partially supported by a grant from the Tel Aviv University Center for AI and Data Science (TAD).

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Authors and Affiliations

  1. Department of Industrial Engineering, Tel Aviv University, Tel Aviv, 6997801, Israel

    Michael Khavkin & Eran Toch

Authors
  1. Michael Khavkin
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  2. Eran Toch
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Corresponding author

Correspondence to Michael Khavkin .

Editor information

Editors and Affiliations

  1. Office Data Protection Commissioner of Schleswig-Holstein (ULD), Kiel, Germany

    Felix Bieker

  2. Tel Aviv University, Tel Aviv, Israel

    Joachim Meyer

  3. Goethe University Frankfurt, Frankfurt, Germany

    Sebastian Pape

  4. Ostfalia Hochschule für Angewandte Wissenschaften, Wolfenbüttel, Germany

    Ina Schiering

  5. Leibniz-Institut für Bildungsmedien | Georg-Eckert-Institut, Braunschweig, Germany

    Andreas Weich

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Khavkin, M., Toch, E. (2023). Valuation of Differential Privacy Budget in Data Trade: A Conjoint Analysis. In: Bieker, F., Meyer, J., Pape, S., Schiering, I., Weich, A. (eds) Privacy and Identity Management. Privacy and Identity 2022. IFIP Advances in Information and Communication Technology, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-031-31971-6_7

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  • DOI: https://doi.org/10.1007/978-3-031-31971-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31970-9

  • Online ISBN: 978-3-031-31971-6

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