Sensing and privacy
Data minimization
Minimisation des données
Data minimization is the practice of limiting collected and retained information to what is necessary for a defined purpose. Curb programs can apply it by reducing identifiers, spatial detail, collection frequency, or retention, while documenting why each remaining field is required and who is authorized to access it.
Understanding the term
Aggregation alone may not eliminate identification risk where observations remain detailed. Review the full data flow, including supplier logs and exports, rather than only the public dashboard.
Validate the measurement against an independent reference under representative conditions. Record uncertainty, coverage, missing intervals, and the difference between a raw observation and an inferred activity. Technical outputs should be used only for purposes they can support reliably. Assign responsibility for calibration, corrections, access, and retention, and assess privacy across the complete data flow. An operational dashboard can conceal measurement errors if it displays precise values without their method or limitations.