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Proceedings on Privacy Enhancing Technologies

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Cross-Cultural Privacy Prediction

Yao Li
  • Corresponding author
  • University of California, Irvine
  • Email:
/ Alfred Kobsa
  • University of California, Irvine
  • Email:
/ Bart P. Knijnenburg
  • Clemson University
  • Email:
/ M-H. Carolyn Nguyen
  • Microsoft Corporation
  • Email:
Published Online: 2017-04-04 | DOI: https://doi.org/10.1515/popets-2017-0019

Abstract

The influence of cultural background on people’s privacy decisions is widely recognized. However, a cross-cultural approach to predicting privacy decisions is still lacking. Our paper presents a first integrated cross-cultural privacy prediction model that merges cultural, demographic, attitudinal and contextual prediction. The model applies supervised machine learning to users’ decisions on the collection of their personal data, collected from a large-scale quantitative study in eight different countries. We find that adding culture-related predictors (i.e. country of residence, language, Hofstede’s cultural dimensions) to demographic, attitudinal and contextual predictors in the model can improve the prediction accuracy. Hofstede’s variables - particularly individualism and indulgence - outperform country and language. We further apply generalized linear mixed-effect regression to explore possible interactions between culture and other predictors. We find indeed that the impact of contextual and attitudinal predictors varies between different cultures. The implications of such models in developing privacy-enabling technologies are discussed.

Keywords: privacy decision; culture; individualism; context

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About the article

Received: 2016-08-31

Revised: 2016-11-30

Accepted: 2016-12-01

Published Online: 2017-04-04

Published in Print: 2017-04-01



Citation Information: Proceedings on Privacy Enhancing Technologies, ISSN (Online) 2299-0984, DOI: https://doi.org/10.1515/popets-2017-0019. Export Citation

© 2017 Yao Li et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

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