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Found 2 papers, 1 papers with code
Privacy Assessment of Federated Learning using Private Personalized Layers
While this scheme has been proposed as local adaptation to improve the accuracy of the model through local personalization, it has also the advantage to minimize the information about the model exchanged with the server.
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks
However, nothing prevents the service provider to infer private and sensitive information about a user such as health or demographic attributes. In this paper, we present DySan, a privacy-preserving framework to sanitize motion sensor data against unwanted sensitive inferences (i. e., improving privacy) while limiting the loss of accuracy on the physical activity monitoring (i. e., maintaining data utility).
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