Show HN: Building an End-to-End Encrypted Shazam with Homomorphic Encryption

https://www.zama.ai/post/encrypted-shazam-using-fully-homomorphic-encryption-concrete-ml-tutorial

by zacchj • 2 years ago

59 8 2 years ago

zama.ai

oulipo• 2 years ago

Really cool!

niekb• 2 years ago

I'd be curious to the training time (per training example) of the logistic regression model in FHE.

binoua2• 2 years ago

Everything is open-source, you can have a look yourself, and experiment! https://github.com/iamayushanand/Concrete_Shazam/blob/main/M...

Here, the training is not done on encrypted values: the songs are public, what is secret is which song(s) you like

rkagerer• 2 years ago

This is awesome but with a mere 1000-song database it would be simpler just to run the whole thing on the client. How well could the approach scale? (eg. To a billion song DB?)

binoua2• 2 years ago

Yes for now, it's 1000 song, which is already awesome if you think about it, no? As it's like 300 ms, one can increase the DB size by a few order of magnitude, certainly. It will scale to billions of songs thanks to hardware accelerators, which are coming. One can google and see that there is a bunch of companies (small or large) working on accelerating FHE computations.

Kiuhrly• 2 years ago

This is awesome but with a mere 1000-song database it would be simpler just to run the whole thing on the client

If you have a Google Pixel phone running the stock OS, you already have this! https://support.google.com/pixelphone/answer/7535326?hl=en#z...

unstatusthequo• 2 years ago

Nice idea, but do I need E2E to identify a song? Seems like a very low threat model for a malicious attacker to know my wife needs that Elton John song.

zacchj• 2 years ago

Yes, but I think it illustrates that FHE has the power to safeguard users' privacy for any app that require mic access... !

Show HN: Building an End-to-End Encrypted Shazam with Homomorphic Encryption

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