Balancing act: Europeans' privacy calculus and security concerns in online CSAM detection.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers in Big Data Pub Date : 2025-01-22 eCollection Date: 2025-01-01 DOI:10.3389/fdata.2025.1477911
Răzvan Rughiniş, Simona-Nicoleta Vulpe, Dinu Ţurcanu, Daniel Rosner
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引用次数: 0

Abstract

This study examines privacy calculus in online child sexual abuse material (CSAM) detection across Europe, using Flash Eurobarometer 532 data. Drawing on theories of structuration and risk society, we analyze how individual agency and institutional frameworks interact in shaping privacy attitudes in high-stakes digital scenarios. Multinomial regression reveals age as a significant individual-level predictor, with younger individuals prioritizing privacy more. Country-level analysis shows Central and Eastern European nations have higher privacy concerns, reflecting distinct institutional and cultural contexts. Notably, the Digital Economy and Society Index (DESI) shows a positive association with privacy concerns in regression models when controlling for Augmented Human Development Index (AHDI) components, contrasting its negative bivariate correlation. Life expectancy emerges as the strongest country-level predictor, negatively associated with privacy concerns, suggesting deep institutional mechanisms shape privacy attitudes beyond individual factors. This dual approach reveals that both individual factors and national contexts are shaping privacy calculus in CSAM detection. The study contributes to a better understanding of privacy calculus in high-stakes scenarios, with implications for policy development in online child protection.

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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
期刊最新文献
On explaining recommendations with Large Language Models: a review. Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization. Balancing act: Europeans' privacy calculus and security concerns in online CSAM detection. A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning. Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis.
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