Data trace as the scientific foundation for trusted metrological data: a review for future metrology direction

In the context of the digital transformation of metrology, ensuring the trustworthiness and integrity of measurement data during its generation, transmission, and storage-i.e., trustworthy detection of measurement data-has become a critical challenge. Data traces are residual marks left during the data processing, which help identify malicious activities targeting measurement data. These traces are especially important when the trust and integrity of potential data evidence are under threat. To this end, this article systematically reviews relevant core techniques and analyzes various detection methods across the different stages of the data lifecycle, evaluating their applicability and limitations in identifying data tampering, unauthorized access, and anomalous operations. The findings suggest that trace detection technologies can enhance the traceability and transparency of metrological data, thereby providing technical support for building a trustworthy digital metrology system. This review lays the theoretical foundation for future research on developing automated anomaly detection models, improving forensic techniques for data tampering in measurement devices, and constructing multi-modal, full-lifecycle traceability frameworks for measurement data. Subsequent studies should focus on aligning these technologies with metrological standards and verifying their deployment in real-world measurement instruments.