Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
Convex-Concave Loss (CCL) is a new loss function that adds a concave term to standard convex losses to increase the variance of training data. This makes it harder for membership inference attacks to distinguish between training and non-training data, improving privacy while maintaining model utility.