In today's digital era, machine learning has become an essential tool for solving complex problems and making data-driven decisions. However, using ML models for sensitive applications can lead to privacy concerns and the potential for data leakage. This becomes especially challenging when the ML model provider needs to offer predictions without disclosing their proprietary model's critical attributes and thresholds. Traditional approaches for sharing ML models expose these sensitive details, making it difficult to strike a balance between functionality and privacy.

So, your marketplace helps to keep the ML Model private and the dataset it has been trained on. It helps the provider earn money through per-request fees he wants to set, keeping the Model private. Other users can get predictions without developing their own ML model by paying a fee to the provider and verifying the proof through zkVerify.

It can be employed in a wide range of applications and industries that require secure and privacy-preserving predictions. Some examples include:
- Healthcare: Safeguard patient privacy while enabling healthcare providers to make data-driven decisions based on ML models. It ensures that personal health information (PHI) and model details are kept confidential, improving patient trust and compliance with data protection regulations.

- Finance: Enhance security and compliance for financial institutions by enabling them to utilize ML models for tasks like credit scoring, fraud detection, and portfolio management, without disclosing sensitive customer data or proprietary model information.

- Human Resources: Streamline the recruitment process by using It to analyze candidate profiles without exposing personal information or revealing details of the underlying ML model, thereby preserving privacy and reducing potential bias.

- Marketing: Leverage customer data for targeted advertising and personalization without compromising user privacy or revealing proprietary ML.