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Zero Knowledge

Episode 321: STIR with Gal Arnon & Giacomo Fenzi

Apr 24, 2024
01:00:22
Snipd AI
Ph.D students Gal Arnon & Giacomo Fenzi discuss their research on 'STIR', a system replacing FRI with optimizations. They explore the history of FRI, proximity testing, proof systems, polynomial folding, coding theory, and advancements in protocols and soundness analysis.
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Podcast summary created with Snipd AI

Quick takeaways

  • STIR improves Snark efficiency by reducing query complexity and enhancing security guarantees.
  • STIR offers a practical solution for decentralized data management with error correction schemes.

Deep dives

Stir: Enhancing Privacy and Scalability in Snarks

Stir introduces a novel approach to improving the efficiency of Snarks, focusing on reducing query complexity and enhancing security guarantees. By combining battle-tested sub-protocols from previous works, Stir offers a drop-in replacement for Fry, leading to improved argument size, verifier time, and query complexity. The paper presents a clear and accessible soundness analysis, making it easier to understand than previous protocols like Fry. While further testing is needed, Stir shows promise as a practical and efficient solution for privacy and scalability in Snarks.

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