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npapernot 2019-07-31 20:40:30 +00:00
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privacy/bolt_on/README.md
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@ -12,7 +12,7 @@ This method uses 4 key steps to achieve privacy guarantees:
2. Projects weights to R, the radius of the hypothesis space,
after each batch. This value is configurable by the user.
3. Limits learning rate
4. Use a strongly convex loss function (see compile)
4. Uses a strongly convex loss function (see compile)
For more details on the strong convexity requirements, see:
Bolt-on Differential Privacy for Scalable Stochastic Gradient