  Machine learning (ML) is revolutionizing research and industry. Many ML
applications rely on the use of large amounts of personal data for training and
inference. Among the most intimate exploited data sources is
electroencephalogram (EEG) data, a kind of data that is so rich with
information that application developers can easily gain knowledge beyond the
professed scope from unprotected EEG signals, including passwords, ATM PINs,
and other intimate data. The challenge we address is how to engage in
meaningful ML with EEG data while protecting the privacy of users.
  Hence, we propose cryptographic protocols based on Secure Multiparty
Computation (SMC) to perform linear regression over EEG signals from many users
in a fully privacy-preserving (PP) fashion, i.e.~such that each individual's
EEG signals are not revealed to anyone else. To illustrate the potential of our
secure framework, we show how it allows estimating the drowsiness of drivers
from their EEG signals as would be possible in the unencrypted case, and at a
very reasonable computational cost. Our solution is the first application of
commodity-based SMC to EEG data, as well as the largest documented experiment
of secret sharing based SMC in general, namely with 15 players involved in all
the computations.
