Federated learning (FL) is a privacy-aware data mining strategy keeping the private on owners’ machine and thereby confidential. The clients compute local models send them to an aggregator which computes global model. In hybrid FL, parameters are additionally masked using secure aggregation, such that only aggregated statistics become available in clear text, not client specific updates. this c...

We prove that quantum-hard one-way functions imply simulation-secure quantum oblivious transfer (QOT), which is known to suffice for secure computation of arbitrary functionalities. Furthermore, our construction only makes black-box use the function. Our primary technical contribution a extractable and equivocal bit commitments based on in standard model. Instantiating Crépeau-Kilian (FOCS 1988...

Secure multiparty computation allows multiple parties to participate in a computation. SMC (secure multiparty computation) assumes n parties where n>1. All the parties jointly compute a function. Privacy preserving data mining has become an emerging field in the secure multiparty computation. Privacy preserving data mining preserves the privacy of individual's data. Privacy preserving data mini...