🎉 We’re thrilled to announce that we’ve won the Privacy Track at ZK Hack Montreal! 🎉

🏆 Prize: $1,000
💻 Track: NovaNet - Privacy
🌐 Project: Privacy-Preserving Machine Learning with Zero-Know

Our project, Privacy-Preserving Machine Learning with Zero-Know, focused on tackling privacy issues in sensitive data environments like healthcare and finance. By using Zero-Knowledge Proofs (ZKPs), we’ve enabled secure collaboration without exposing sensitive data, ensuring privacy and security in critical industries.

🔒 What We Built:
Using NovaNet’s zkEngine with privacy enabled, we developed a zkWasm app that empowers privacy-centric use-cases. Our solution ensures trustless collaboration, allowing multiple parties to perform machine learning and data analysis without sharing sensitive data. This innovation opens new doors for cooperation in industries where data security is paramount, enabling more efficient and secure outcomes.

🚧 Challenges We Faced:
While working on this project, we encountered challenges with proof aggregation, a feature we plan to tackle in the future once the NovaNet library further evolves. We’re excited to explore new folding schemes for improved interoperability down the line.

👥 Team:

-Guillaume Lauzier: @guillaumelauzier

-Vladimir Komendantskiy: @vkomenda

🔗 Project Links:

-GitHub: zkML-Montreal

-GitHub: Hugging Face - Candle

-GitHub: NovaNet zkEngine

A big thank you to the ZK Hack Montreal team and NovaNet for the opportunity to work on this exciting challenge, and to everyone who contributed to this fantastic hackathon!

Stay tuned for future developments as we continue refining our privacy-preserving solutions! 🚀

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