Context-Aware Dataset Sharing and Trading System

THE CHALLENGE

The core challenge for businesses in data-driven sectors like manufacturing, healthcare, and finance is how to securely and effectively collaborate on AI development without exposing sensitive or proprietary data. While combining datasets from different organizations can significantly improve AI model accuracy, current approaches such as federated learning, encrypted computation, or data marketplaces often fall short. They either impose high costs, require matching technical setups, or fail to predict whether sharing data will actually benefit a specific AI task. This results in missed opportunities, inefficient data exchanges, and potential privacy risks. On top of that, data from different sources often come in incompatible formats or lack key features, making integration even harder. Businesses need smarter, context-aware tools that can assess in advance whether and how another party’s data will improve performance without compromising confidentiality or wasting resources.

OUR SOLUTION

Our solution enables businesses to safely and intelligently collaborate on AI development without exposing sensitive data. Instead of sharing raw datasets, each participant transforms their data into a compact, privacy-preserving proxy using specialized neural encoders and a self-attention mechanism that identifies which features matter most for a specific task. These proxies, along with task-aware metadata, are shared within a secure network modeled as a directed graph, where each participant is a node. A Graph Neural Network then predicts which data exchanges will deliver real performance gains. This approach balances confidentiality with collaboration, allowing organizations to make smarter, data-driven decisions about partnerships boosting AI outcomes while protecting proprietary information. It's a scalable, context-aware framework designed for sectors like manufacturing, healthcare, and finance where data value is high but privacy is at highest priority.

Figure: Data sharing between manufacturing stages.

Advantages:

• Privacy-preserving sharing using proxy datasets and metadata
• Task-specific, performance-driven data exchange recommendations
• Native support for heterogeneous and incomplete datasets
• Scalable, low-overhead integration for multi-party data marketplaces
• Dataset search to quickly identify potential data sources for local AI tasks

Potential Application:

• Privacy-preserving healthcare data exchange
• Collaborative manufacturing defect prediction
• Supply chain data sharing and decision-making
• Smart city sensor data sharing and optimization
• Autonomous vehicle data trading
• Financial dataset data trading