What does differential privacy add?
Differential privacy provides a formal bound on how much a computation can change when one record is included. Stronger protection can reduce model utility, so the privacy and performance trade-off must be measured.
Answered in
NIST’s Federated-Learning Testbed Puts Privacy Claims on a Measurable TrackNIST compares federated learning and differential privacy on soybean genomic data, while showing that local records do not guarantee privacy.
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