Show HN: Building an End-to-End Encrypted Shazam with Homomorphic Encryption(zama.ai)
zama.ai
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
9 comments
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?)
> 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...
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...
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.
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.
Yes, but I think it illustrates that FHE has the power to safeguard users' privacy for any app that require mic access... !
I'd be curious to the training time (per training example) of the logistic regression model in FHE.
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
Here, the training is not done on encrypted values: the songs are public, what is secret is which song(s) you like