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FLaPS: Federated Learning and Privately Scaling
Sudipta Paul, Poushali Sengupta, Subhankar Mishra
Federated learning (FL) is a distributed learning process where the model (weights and checkpoints) is transferred to the devices that posses data rather than the classical way of…
Learning With Differential Privacy
Poushali Sengupta, Sudipta Paul, Subhankar Mishra
The leakage of data might have been an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint pro…
BUDS: Balancing Utility and Differential Privacy by Shuffling
Poushali Sengupta, Sudipta Paul, Subhankar Mishra
Balancing utility and differential privacy by shuffling or \textit{BUDS} is an approach towards crowd-sourced, statistical databases, with strong privacy and utility balance using…