Why do we need privacy computing
The Data Privacy Law requires that data cannot be shared casually. But if the data is not connected, it cannot be used. Privacy computing solves this contradiction.
Federated Learning
The data remains unchanged and the model moves. All parties' data is not exported locally, only model parameters are exchanged, and the model is jointly trained.
Differential Privacy
Add noise to the data to prevent individual data from being recognized, but preserve the overall statistical features.
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