[pixelcort]

In addition to Differential Privacy, Secure Multiparty Computation is another way to maintain privacy, while allowing computation across multiple users.

https://en.m.wikipedia.org/wiki/Secure_multi-party_computati...

The benefit of this is that you can get an exact computation, whereas with differential privacy the output is rougher.

The benefit of differential privacy is that it does not rely on the trust of a majority of other users; you can theoretically verify that a certain percent of the time your device sends out a wrong answer.

[sangel]

I think you are a bit confused. They are very different in what they guarantee.

The goal of MPC is to hide the inputs of the program. But it is okay for an adversary to make all sorts of inferences by looking at the outputs.

The goal of differential privacy is to limit the kind of inferences that an adversary can make about a particular user/input from the output itself.

[v4dok]

MPC works only hand-in-hand with DP. MPC ensures that you don't have access to the "remote" dataset, but does nothing to mitigate model "memorising" specific private information (if we are talking about advanced analytics that is)

[perone]

The only issue is that you won't be able to find reasonable libraries for it, all of them are just PoCs without testing or stability.

[aloknnikhil]

Kyber has a pretty solid implementation of Shamir Sharing. But Shamir Sharing itself has security concerns.

https://godoc.org/gopkg.in/dedis/kyber.v2/share

https://github.com/dedis/kyber

[ginger_beer_m]

Check out https://www.openmined.org