Secure and Scalable Statistical Computation of Questionnaire Data in R

Yigzaw, K.Y., Michalas, A. and Bellika, J. 2016. Secure and Scalable Statistical Computation of Questionnaire Data in R. IEEE Access. 4, pp. 4635-4645. doi:10.1109/ACCESS.2016.2599851

TitleSecure and Scalable Statistical Computation of Questionnaire Data in R
TypeJournal article
AuthorsYigzaw, K.Y., Michalas, A. and Bellika, J.
Abstract

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model.

The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks.

The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.

KeywordsBloom Filter
Privacy
Questionnaire
Statistical Analysis
Secure Multi-Party Computation
Secret Sharing
JournalIEEE Access
Journal citation4, pp. 4635-4645
ISSN2169-3536
Year2016
PublisherIEEE
Publisher's version07542506.pdf
Digital Object Identifier (DOI)doi:10.1109/ACCESS.2016.2599851
Web address (URL)http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7542506
Publication dates
Published12 Aug 2016

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