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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, 2. Projects weights to R, the radius of the hypothesis space,
after each batch. This value is configurable by the user. after each batch. This value is configurable by the user.
3. Limits learning rate 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: For more details on the strong convexity requirements, see:
Bolt-on Differential Privacy for Scalable Stochastic Gradient Bolt-on Differential Privacy for Scalable Stochastic Gradient