Data Component Method Based on Dual-Factor Ownership Identification with Multimodal Feature Fusion
Shenghao Nie, Jin Shi, Xiaoyang Zhou, Mingxin Lu · Sensors · 2025
In the booming digital economy, data circulation-particularly for massive multimodal data generated by IoT sensor networks-faces critical challenges: ambiguous ownership and broken cross-domain traceability. Traditional property rights theory, ill-suited to data's non-rivalrous nature, leads to ownership fuzziness after multi-source fusion and traceability gaps in cross-organizational flows, hindering marketization. This study aims to establish native ownership confirmation capabilities in trusted IoT-driven data ecosystems. The approach involves a dual-factor system: the collaborative extraction of text (from sensor-generated inspection reports), numerical (from industrial sensor measurements), visual (from 3D scanning sensors), and spatio-temporal features (from GPS and IoT device logs) generates unique SHA-256 fingerprints (first factor), while RSA/ECDSA private key signatures (linked to sensor node identities) bind ownership (second factor). An intermediate state integrates these with metadata, supported by blockchain (consortium chain + IPFS) and cross-domain protocols optimized for IoT environments to ensure full-link traceability. This scheme, tailored to the characteristics of IoT sensor networks, breaks traditional ownership confirmation bottlenecks in multi-source fusion, demonstrating strong performance in ownership recognition, anti-tampering robustness, cross-domain traceability and encryption performance. It offers technical and theoretical support for standardized data components and the marketization of data elements within IoT ecosystems.