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Data Component Method Based on Dual-Factor Ownership Identification with Multimodal Feature Fusion.

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  • 1School of Information Management, Nanjing University, 163#XianLin Dadao, Nanjing 210023, China.

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Summary

This study introduces a dual-factor system for confirming data ownership and traceability in Internet of Things (IoT) ecosystems. It enhances data marketization by resolving ownership ambiguity and ensuring secure, verifiable data flows.

Keywords:
cross-domain data traceabilitydata element marketizationdual-factor ownership confirmationmultimodal feature fusion

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Area of Science:

  • Computer Science
  • Data Science
  • Cybersecurity

Background:

  • Massive multimodal data from IoT sensor networks presents challenges in ownership and traceability.
  • Traditional property rights are inadequate for data's non-rivalrous nature, causing marketization hurdles.
  • Existing systems struggle with ownership fuzziness and traceability gaps in cross-organizational data flows.

Purpose of the Study:

  • To establish native ownership confirmation capabilities within trusted IoT-driven data ecosystems.
  • To address bottlenecks in multi-source data fusion and cross-domain traceability.
  • To support standardized data components and facilitate data marketization in IoT.

Main Methods:

  • A dual-factor system combining SHA-256 fingerprints from extracted features (text, numerical, visual, spatio-temporal) and RSA/ECDSA private key signatures.
  • Integration of features and metadata using blockchain (consortium chain + IPFS) and optimized cross-domain protocols.
  • Utilizing sensor-generated inspection reports, industrial measurements, 3D scans, GPS, and IoT device logs.

Main Results:

  • Demonstrated strong performance in ownership recognition and anti-tampering robustness.
  • Achieved full-link traceability across cross-domain data flows.
  • Validated effective encryption performance and resolution of ownership confirmation bottlenecks.

Conclusions:

  • The proposed scheme effectively confirms data ownership and enhances traceability in IoT environments.
  • It provides a robust framework for standardized data components and data marketization.
  • The system offers technical and theoretical support for secure and verifiable data circulation in the digital economy.