Cross-Cultural Privacy Prediction
Open accessAbstract
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.
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The difference in memory's privacy preferences may be subject to power distance and culture norms.
“The difference in memory's privacy preferences may be subject to the power distance [9] and culture norms [18].”
From Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory (2025) · cites this paper as [18]
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Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
Zhang, S., Ma, R., Ma, Y., Li, S., Xu, Y., Yi, X., & Li, H. (2025) · Proceedings of the 2025 Workshop on Human-Centered AI Privacy and Security · doi:10.1145/3733816.3760750 · Cited by 6 · see citing works
- Authors
- Li, Y., Kobsa, A., Knijnenburg, B. P., & Carolyn Nguyen, M.-H.
- Affiliations
- University of California, Irvine (US)
- Clemson University (US)
- Microsoft Corporation
- Venue
- Proceedings on Privacy Enhancing Technologies
- Published
- 2017-04-01
- Volume
- 2017
- Issue
- 2
- Pages
- 113-132
- ISSN
- 2299-0984 (ISSN-L)
- OpenAlex
- W2605025788
- Cited by
- 84
- References
- 50
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 10 · 2023: 7 · 2022: 14 · 2021: 8 · 2020: 7 · 2019: 9 · 2018: 9 · 2017: 1
- FWCI
- 18.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings on Privacy Enhancing Technologies (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Psychology › Social Psychology › Cultural Differences and Values
- Keywords
- privacy decisions, cultural dimensions, Hofstede cultural dimensions, individualism, indulgences, personal data collection, demographic predictors, contextual predictors, language, country of residence
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