Very related to differential privacy: https://research.googleblog.com/2015/08/the-reusable-holdout... (together with IBM and Samsung)
Actually Microsoft has been publishing a lot on privacy enhancing Machine Learning too. For instance: https://www.microsoft.com/en-us/research/publication/crypton...…
Because you don't have capital, risk-adversity, and access to the (expensive) dataset. You only have encrypted predictions, which are worthless for you.
You don't really need to know what the features mean to make a good model. Even if only you knew the meaning of the features you'd still get beaten by someone using a blackbox approach. This is exactly what happened to…
Very related to differential privacy: https://research.googleblog.com/2015/08/the-reusable-holdout... (together with IBM and Samsung)
Actually Microsoft has been publishing a lot on privacy enhancing Machine Learning too. For instance: https://www.microsoft.com/en-us/research/publication/crypton...…
Because you don't have capital, risk-adversity, and access to the (expensive) dataset. You only have encrypted predictions, which are worthless for you.
You don't really need to know what the features mean to make a good model. Even if only you knew the meaning of the features you'd still get beaten by someone using a blackbox approach. This is exactly what happened to…