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0 pointskuzehanka7y ago0 comments

Go on, tell us how it works so we can all start doing the same and save millions of dollars in risk/compliance crossing enablement.

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3 comments · 1 top-level

nevir7y ago· 2 in thread

https://cloud.google.com/solutions/sensitive-data-and-ml-dat... has some high level guidance on different techniques that can be applied, depending on the type of data you're working with.

kuzehankaOP7y ago

I actually know this article, so I'll summarise it for the readers.

It covers de-risking data if it isn't required for ML models or is of low impact. Through removal of it, masking of it, or entropy injection to it. Every single one of these approaches is detrimental to an ML model and just make it useless unless the fields being controlled are't correlated to the outcome in the first place.

This article is an acknowledgement of the issue and the fact that solutions don't exist. Only risk management strategies which disable potential solutions.

Their ultimate answer is 'protect the data and give it to only a small restricted set of people'. Right back where we started.

Fireflite7y ago

Injecting entropy into your data in order to generate differentially private access actually typically improves model performance, rather than degrades it.

This behaviour is counterintuitive, but effectively you're enforcing a degree of regularization in a way similar to how data augmentation works. You'll get reduced training set performance and improved test set performance, as with other forms of regularization until you make it too strong.

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