Behavioral Opacity in Physical Systems: A Framework for Reducing Analytical Legibility in Connected Vehicle Environments

This paper proposes a structural inversion in the dominant framework governing privacy in connected vehicle environments. Rather than treating data collection as the central risk and regulatory constraint as the solution, it argues that the primary threat to individual autonomy is data interpretability — the capacity of collected data to sustain reliable behavioral inference — and introduces the concept of the opacity threshold as the point below which a behavioral profile falls beneath the reliability threshold of commercial predictive systems. Building on the behavioral opacity framework developed in Maschietto (2026a) and (2026b), the paper extends the framework to physical mobility systems. It identifies four structural variables that determine analytical density — temporal consistency, identity coherence, semantic density, and session continuity — and examines how behavioral choices operating within lawful system participation can modify profile legibility without requiring technical countermeasures or regulatory intervention. The paper additionally identifies a critical gap in current data regulation: while the GDPR, the EU Data Act, and NIS2 protect against unlawful data processing, none address the analytical exploitation of lawfully collected, high-density behavioral profiles. In jurisdictions where even the prior condition for interpretability-aware regulation is absent — mandatory disclosure of collection practices — the paper provides the theoretical foundation for the sectoral transparency instruments that must precede regulatory evolution. A legislative proposal addressing this foundational layer for the Brazilian context is developed in the companion policy document (Maschietto, 2026e).