A Computational Framework for Automated Reconstruction and Analysis of Dynamic Consent Interaction

Area of Science:

• * Computational Social Science
• * Human-Computer Interaction
• * Privacy Engineering

Background:

• * Dynamic consent ecosystems are increasingly complex, driven by Consent Management Platforms (CMPs) and adaptive interfaces.
• * Existing privacy auditing methods struggle with dynamic interactions due to static inspection limitations.
• * Reconstructing and evaluating complex, multi-layered consent processes remains a significant challenge.

Purpose of the Study:

• * To introduce a computational measurement framework for automated reconstruction and analysis of dynamic consent ecosystems.
• * To address limitations in current privacy auditing by enabling dynamic interaction evaluation.
• * To operationalize and identify Adaptive Cognitive Load Dark Patterns (ACL-DPs) within consent flows.

Main Methods:

• * Integration of five computational layers: browser acquisition, interaction sensing, multi-layer synchronization, consent-state verification, and ACL-DP operationalization.
• * Utilization of asynchronous browser automation, interaction workflow reconstruction, and multi-source evidence synchronization.
• * Backend consent verification and rule-based scoring for mechanism analysis.

Main Results:

• * Evaluation of 18,665 consent ecosystems yielded 59 synchronized variables.
• * Workflow reconstruction achieved 99.6% success; backend verification found consent mismatches in 77.6% of cases.
• * Identified dominant manipulation strategies including Effort Engineering, revealing procedural/attentional asymmetries and frontend-backend inconsistencies.

Conclusions:

• * The proposed framework provides a reproducible method for large-scale privacy interaction analysis.
• * Significant divergence exists between frontend consent presentation and backend data processing.
• * Findings highlight the need for robust auditing of dynamic consent mechanisms to ensure user privacy.