Modernizing User Privacy Preference Measurement through GPPI: A GDPR-aligned Privacy Preference Item Bank

2026-05-22

AI Summary

This study addresses the gap in existing privacy measurement tools, which lack fine-grained dimensions aligned with specific General Data Protection Regulation (GDPR) rights—such as data portability and the right to erasure—and thus struggle to assess users’ genuine preferences regarding their statutory entitlements. To bridge this gap, the authors systematically derived 669 statements from all 99 GDPR articles, integrating legal text analysis, two rounds of expert consensus review (requiring ≥4/5 agreement), semantic clustering (yielding 10 main themes and 87 sub-themes), and high-threshold filtering. This process yielded the GDPR Privacy Preference Inventory (GPPI), a novel measurement instrument comprising 527 items across 9 main themes and 73 sub-themes, with an average expert agreement rate of 85%. The GPPI effectively distinguishes between general “privacy concerns” and specific “statutory rights preferences,” thereby enabling quantitative assessment of user values in GDPR compliance practice.

Abstract

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.

Problem

• privacy preference
• GDPR
• measurement instrument
• regulatory mechanisms
• user valuation

Innovation

• GDPR-aligned measurement
• privacy preference item bank
• semantic clustering
• expert consensus validation
• regulatory mechanism alignment