Zero knowledge proofs enhance transparency while maintaining confidentiality in decision-making processes. Lagrange is pioneering the integration of zero knowledge proofs in AI to ensure privacy. Current privacy methods in AI fall short compared to innovative cryptographic approaches.
Key takeaways
- Zero knowledge proofs enhance transparency while maintaining confidentiality in decision-making processes.
- Lagrange is pioneering the integration of zero knowledge proofs in AI to ensure privacy.
- Current privacy methods in AI fall short compared to innovative cryptographic approaches.
- Privacy-preserving models require cryptographic integration from the development stage.
- Open source models often underperform in commercial applications.
- Protecting both intellectual property and consumer data is crucial in AI model deployment.
- Many private AI solutions fail to enhance privacy on commercially relevant models.
- The tech industry’s focus on trivial applications detracts from addressing national security.
- ZK machine learning relies on mathematics, not hardware, for privacy.
- Venture financing in aerospace and defense is insufficient for national security needs.
- Lagrange’s Deep Proof library is a key innovation in safeguarding AI data.
- Zero knowledge technology is reshaping the landscape of cryptographic security.
Guest intro
Ismael Hishon-Rezaizadeh is CEO and co-founder of Lagrange Labs, where he leads the development of DeepProve, the world’s fastest zkML library for verifiable AI inference. He spearheaded the first zero-knowledge proof of Google’s Gemma3 large language model, demonstrating production-ready verification at 158x the performance of competing solutions. His work advances ZK technology from crypto applications to defense-critical uses like securing autonomous drone swarms.
