Zero Knowledge cover image

Episode 265: Where ZK and ML intersect with Yi Sun and Daniel Kang

Zero Knowledge

Zero Knowledge Machine Learning (ZKML) and its Practical Application

55sec Snip

00:00
Play full episode
ZKML aims to generate zero knowledge proofs that a machine learning model has run on specific input data. This method inherits the benefits of zero knowledge proofs such as succinctness, zero knowledge, completeness, and soundness. One practical application of ZKML is to prove the accurate execution of machine learning models, especially when the models are operated behind an API interface. For instance, it can be used to verify that an ML provider is keeping their model weights confidential when processing external data.

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.
App store bannerPlay store banner

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export

Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode