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Multiparty Computation with Statistical Input Confidentiality via Randomized Response

  • Josep Domingo-Ferrer
  • Rafael Mulero-Vellido
  • Jordi Soria-ComasEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11126)

Abstract

We explore a setting in which a number of subjects want to compute on their pooled data while keeping the statistical confidentiality of their input. Statistical confidentiality is different from the cryptographic confidentiality guaranteed by cryptographic multiparty secure computation: whereas in the latter nothing is disclosed about the input, in statistical input confidentiality a noise-added version of the input is disclosed, which allows more flexible computations. We propose a protocol based on local anonymization via randomized response, whereby the empirical distribution of the data of the subjects is approximated. From that distribution, most statistical calculations can be approximated as well. Regarding the accuracy of the approximation, ceteris paribus it improves with the number of subjects. Large dimensionality (that is, a large number of attributes) decreases accuracy and we propose a strategy to mitigate the dimensionality problem. We show how to characterize the privacy guarantee for each subject in terms of differential privacy. Experimental work is reported on the attained accuracy as a function of the number of respondents, number of attributes and randomized response parameters.

Keywords

Multiparty anonymous computation Randomized response Local anonymization Big data Privacy 

Notes

Acknowledgments and Disclaimer

The following funding sources are gratefully acknowledged: European Commission (H2020-700540 “CANVAS”), Government of Catalonia (ICREA Acadèmia Prize to J. Domingo-Ferrer) and Spanish Government (projects TIN2014-57364-C2-1-R “SmartGlacis” and TIN2015-70054-REDC). The views in this paper are the authors’ own and do not necessarily reflect the views of UNESCO or any of the funders.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Josep Domingo-Ferrer
    • 1
  • Rafael Mulero-Vellido
    • 1
  • Jordi Soria-Comas
    • 1
    Email author
  1. 1.Department of Computer Science and Mathematics, UNESCO Chair in Data Privacy, CYBERCAT-Center for Cybersecurity Research of CataloniaUniversitat Rovira i VirgiliTarragonaSpain

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