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Privacy-aware data processing and fair model trading protocols
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PRE
AI
DOI：10.1016/j.ins.2025.122946.png)
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Abstract
En 中文
Data has become a foundational element driving the digital economy and artificial intelligence. However, the current data sharing situation remains suboptimal. A key barrier is the lack of mutual trust between data providers and data processors. Data providers are concerned about the potential disclosure of sensitive information contained in raw data, while data processors worry about unfair transactions, such as the theft or misuse of their models without payment. Additionally, data providers need to verify the quality of the models without revealing. To address these challenges, we propose a privacy-preserving model outsourcing and fair trading scheme, where all participants including data processors and data providers, can be un-trusted. This framework separates data ownership, algorithm execution, and verification, allowing data buyers (for processed data instead of raw data) to validate the model performance without accessing raw data, thus preventing leakage and resale. We introduce three core protocols: data validation protocol, algorithm validation protocol, and fairness arbitration protocol, which ensure transaction integrity, protect privacy, and secure fair compensation. Through extensive security analysis and dynamic game theory analysis, we demonstrate the security and fairness that maximize benefits for all parties involved.
Keywords:
Data trading
Privacy protection
UC security framework
Dynamic game theory
Journal
IF:
6.8
Papers:
553
Citations:
6.2W
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