Secure multiparty computation enables privacy-preserving linear regression on multi-user EEG signals for driver drowsiness estimation, demonstrated with 15 parties at reasonable computational cost.
Universally composable and statistically secure verifiable secret sharing scheme based on pre-distributed data
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Protecting Privacy of Users in Brain-Computer Interface Applications
Secure multiparty computation enables privacy-preserving linear regression on multi-user EEG signals for driver drowsiness estimation, demonstrated with 15 parties at reasonable computational cost.