Random activation and Top-k sparsification are claimed to amplify differential privacy in decentralized non-convex optimization, reducing required noise by a factor of the sparsification ratio times the square of the activation probability.
Membership inference attacks against machine learning models via prediction sensitivity,
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Decentralized Optimization with Amplified Privacy via Efficient Communication
Random activation and Top-k sparsification are claimed to amplify differential privacy in decentralized non-convex optimization, reducing required noise by a factor of the sparsification ratio times the square of the activation probability.