  Human brain activity collected in the form of Electroencephalography (EEG),
even with low number of sensors, is an extremely rich signal. Traces collected
from multiple channels and with high sampling rates capture many important
aspects of participants' brain activity and can be used as a unique personal
identifier. The motivation for sharing EEG signals is significant, as a mean to
understand the relation between brain activity and well-being, or for
communication with medical services. As the equipment for such data collection
becomes more available and widely used, the opportunities for using the data
are growing; at the same time however inherent privacy risks are mounting. The
same raw EEG signal can be used for example to diagnose mental diseases, find
traces of epilepsy, and decode personality traits. The current practice of the
informed consent of the participants for the use of the data either prevents
reuse of the raw signal or does not truly respect participants' right to
privacy by reusing the same raw data for purposes much different than
originally consented to. Here we propose an integration of a personal
neuroinformatics system, Smartphone Brain Scanner, with a general privacy
framework openPDS. We show how raw high-dimensionality data can be collected on
a mobile device, uploaded to a server, and subsequently operated on and
accessed by applications or researchers, without disclosing the raw signal.
Those extracted features of the raw signal, called answers, are of
significantly lower-dimensionality, and provide the full utility of the data in
given context, without the risk of disclosing sensitive raw signal. Such
architecture significantly mitigates a very serious privacy risk related to raw
EEG recordings floating around and being used and reused for various purposes.
