In this episode, Stanford cryptography professor Dan Boneh and a16z General Partner Ali Yahya elevate our knowledge of zero-knowledge proofs and their applications in the blockchain world. Listen as they dive into the intricacies of succinct proof systems, the difference between SNARKs and STARKs, the magic of recursive SNARKs, and why zero-knowledge systems are essential to the evolution of Ethereum.
Listen to the episode on Apple Podcasts, Spotify, Overcast, Podcast Addict, Pocket Casts, Stitcher, Castbox, Google Podcasts, TuneIn, Amazon Music, or on your favorite podcast platform.
Show highlights:
- how Ali became a general partner at a16z Crypto and why Dan started working on “the science of blockchains”
- what a succinct proof system is
- analogies for understanding zero-knowledge proofs
- the difference between SNARKs and STARKs and whether centralization can be fully avoided
- how zero-knowledge technology became so crucial for blockchains
- the reasons to push computations off-chain and the applications of this technology
- why zkEVMs are essential to help Ethereum scale
- why privacy is important not only in financial transactions but also in other areas like social networks and gaming
- the challenges that arise from trusted setups and whether it would be possible to create false proofs
- how to mitigate the trusted setup problem with different proof systems
- what is being built to make zero-knowledge proofs cheaper to create
- whether a privacy-focused technology can be pursued while staying compliant with regulations
- how zero-knowledge proofs can improve the security of blockchain bridges
Thank you to our sponsors!
Guests:
- Ali Yahya, general partner at a16z crypto
- Dan Boneh, professor of computer science and electrical engineering, Stanford University; and senior research advisor, a16z crypto
Links
- Unchained: zkEVM: The Computing Overhaul to Help Scale Ethereum
- Previous coverage of Unchained on zero-knowledge:
- a16z crypto:
- CoinDesk: Polygon Rolls Out Zero-Knowledge, Privacy-Enhanced Identification Product