Privacy Against Inference Attacks in Vertical Federated Learning

  title={Privacy Against Inference Attacks in Vertical Federated Learning},
  author={Borzoo Rassouli and Morteza Varasteh and Deniz G{\"u}nd{\"u}z},
Vertical federated learning is considered, where an active party, having access to true class labels, wishes to build a classification model by utilizing more features from a passive party, which has no access to the labels, to improve the model accuracy. In the prediction phase, with logistic regression as the classification model, several inference attack techniques are proposed that the adversary, i.e., the active party, can employ to reconstruct the passive party’s features, regarded as… 

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