Modernizing User Privacy Preference Measurement through GPPI: A GDPR-aligned Privacy Preference Item Bank
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918519449321472 |
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| author | Hmaiti, Yahya Maslych, Mykola Ghasemaghaei, Amirpouya Dang, Trung Cuong Pittman, Corey Mohaisen, David LaViola Jr, Joseph J. |
| author_facet | Hmaiti, Yahya Maslych, Mykola Ghasemaghaei, Amirpouya Dang, Trung Cuong Pittman, Corey Mohaisen, David LaViola Jr, Joseph J. |
| contents | Privacy measurement instruments (e.g., CFIP, IUIPC, PAQ) predate GDPR by over a decade and measure privacy concerns, distinct from preferences for regulatory protections (e.g., data portability, erasure, automated decision-making rights). This leaves practitioners without tools to assess whether users value the GDPR mechanisms implemented in compliant policies. We developed a GDPR-grounded privacy preference measurement item bank by extracting 669 statements from all 99 GDPR articles, validated by: (1) two-round expert review achieving full consensus on accuracy, (2) semantic clustering into 10 parent themes and 87 subthemes, and (3) consensus review with 50 privacy experts (5 per theme) using a larger or equal than 4/5 vote retention threshold. The final 527-item bank comprises 9 parent themes and 73 subthemes (18 to 112 items per parent theme, 1 to 29 per subtheme), enabling targeted measurement across granularities while covering GDPR at mean pairwise expert agreement of approx. 85%. This work introduces a complementary measurement dimension aligning user preferences with regulatory mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24307 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modernizing User Privacy Preference Measurement through GPPI: A GDPR-aligned Privacy Preference Item Bank Hmaiti, Yahya Maslych, Mykola Ghasemaghaei, Amirpouya Dang, Trung Cuong Pittman, Corey Mohaisen, David LaViola Jr, Joseph J. Human-Computer Interaction Cryptography and Security Computers and Society Privacy measurement instruments (e.g., CFIP, IUIPC, PAQ) predate GDPR by over a decade and measure privacy concerns, distinct from preferences for regulatory protections (e.g., data portability, erasure, automated decision-making rights). This leaves practitioners without tools to assess whether users value the GDPR mechanisms implemented in compliant policies. We developed a GDPR-grounded privacy preference measurement item bank by extracting 669 statements from all 99 GDPR articles, validated by: (1) two-round expert review achieving full consensus on accuracy, (2) semantic clustering into 10 parent themes and 87 subthemes, and (3) consensus review with 50 privacy experts (5 per theme) using a larger or equal than 4/5 vote retention threshold. The final 527-item bank comprises 9 parent themes and 73 subthemes (18 to 112 items per parent theme, 1 to 29 per subtheme), enabling targeted measurement across granularities while covering GDPR at mean pairwise expert agreement of approx. 85%. This work introduces a complementary measurement dimension aligning user preferences with regulatory mechanisms. |
| title | Modernizing User Privacy Preference Measurement through GPPI: A GDPR-aligned Privacy Preference Item Bank |
| topic | Human-Computer Interaction Cryptography and Security Computers and Society |
| url | https://arxiv.org/abs/2605.24307 |