IEEE Technology and Society Magazine - June 2015 - 37

to keep up with Privacy-by-Design [42] values, as well as
with general values of privacy-enhancing technologies.
We view the development of prevention and mitigation tools as an interdisciplinary effort, involving neuroscientists, neural engineers, ethicists, as well as legal,
security, and privacy experts. Isolated, individual expertise in each of these areas is unlikely to result in an
effective approach to security; a more comprehensive
understanding is required, and multiple disciplines can
help deliver that understanding.
The first step of an interdisciplinary approach should
be an open discussion, aimed at answering the following questions: a) Who should be allowed access to
an individuals' neural signals? b) Which components
of these neural signals should those entities have an
access to? c) How noisy, distorted or distilled should
these components be made before making them available? d) Which purposes are the entities allowed to use
the neural signals for? and e) What are the risks associated with the misuse of the provided components, i.e.,
what amount of private information can be extracted
from the provided components?
We propose a "triangle" approach towards enhancing privacy and security of BCI technology. This model
is meant to emphasize three aspects of security that
should be interrelated: prescriptive, theoretical, and
developer aspects.

On one vertex of the triangle, the prescriptive vertex,
we place legal experts and ethicists, defining a set of laws
and policies to govern legitimate use of neural signals. As
an example of possible legal intervention, the law could
examine whether BCI platforms should indeed be immunized for the apps they sell, or is some other balance
between manufacturers and application developers more
appropriate for BCI technologies.
On the second vertex, the theoretical vertex, we place
a group of neuroscientists and engineers, in charge of
developing and establishing a set of industry and research
standards, methods, processes, and practices for secure
and privacy-preserving BCI systems. One such practice
may, for example, require there to exist a centralized
authority in charge of reviewing and validating every BCI
application before allowing its use in general population.
Finally, at the third vertex, the developer vertex, we
place BCI systems manufacturers and application developers. These parties are developing, implementing, and
using engineering practice, methods, and tools, in order
to prevent and mitigate specific classes of security and
privacy attacks.

BCI Anonymizer
One engineering approach to enhancing neural privacy and
security is the use of the BCI Anonymizer described in a
patent application by two of the authors of this article [43].

Structure of a Compromised BCI System
Signal Processing
Signal
Acquisition

Digitized
Signal

Feature
Extraction

Translation
Algorithm

Malicious
Feature
Extraction

Malicious
Classification
Algorthim

Extracted Private
Data

Malicious BCI Application
Legend
"Brain Malware" by Hijacking Legitimate BCI Components
"Brain Malware" by Implementing Additional Algorithms for Malicious Application
Figure 2. A simplified diagram of a compromised BCI system. We distinguish between two types of attackers: a) an attacker who
exploits the legitimate feature extraction and decoding (translation) algorithms (denoted as orange blocks in the diagram), and b) an
attacker who implements an additional malicious application, and either replaces or supplements the legitimate BCI resources (denoted as
red blocks in the diagram).

june 2015

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