The problem Zero Knowledge Decision Tree Prediction (ZK-DTP) solves

Problem:

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.

Solution:

Zero Knowledge Decision Tree Predict is designed to address this pressing issue by offering privacy-preserving predictions using decision tree models, built on top of RISC Zero's zkVM. ZK-DTP enables ML model providers to generate accurate predictions without revealing any sensitive information about the model or the input data.

Use Cases:

Healthcare: Safeguard patient privacy while enabling healthcare providers to make data-driven decisions based on ML models. ZK-DTP 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.

Benefits:

Privacy-Preservation, Trustworthy Predictions, Ease of Integration, Scalability