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DeepCog A Trustworthy Deep Learning-Based Human Cognitive Privacy Framework in Industrial Policing.pdf
1. DeepCog: A Trustworthy Deep Learning
Based Human Cognitive Privacy
Framework in Industrial Policing
Abstract
The proliferation of the Internet of Things (IoT) has led to the design and
incorporation of innovative user control mechanisms, one category based on
brain-derived biometric data and known as Brain Control Interface (BCI). BCI
devices measure brain signal
computerised systems, such as the Industrial Internet of Things (IIoT),
intuitively. However, the utilisation of EEG data in the IIoT for actuator control
and the collection of such biometric data as evidence for p
introduces considerable implications for the users’ cognitive privacy. Thus,
considering the importance of cognitive privacy in an evolving era of smart
environments, it becomes imperative to develop methods that can ensure the
cognitive privacy of users. Cognitive privacy also protects the anonymity of
DeepCog: A Trustworthy Deep Learning
Based Human Cognitive Privacy
Framework in Industrial Policing
The proliferation of the Internet of Things (IoT) has led to the design and
incorporation of innovative user control mechanisms, one category based on
derived biometric data and known as Brain Control Interface (BCI). BCI
devices measure brain signals in EEG and allow users to interact with
computerised systems, such as the Industrial Internet of Things (IIoT),
intuitively. However, the utilisation of EEG data in the IIoT for actuator control
and the collection of such biometric data as evidence for policing the IIoT
introduces considerable implications for the users’ cognitive privacy. Thus,
considering the importance of cognitive privacy in an evolving era of smart
environments, it becomes imperative to develop methods that can ensure the
rivacy of users. Cognitive privacy also protects the anonymity of
DeepCog: A Trustworthy Deep Learning-
The proliferation of the Internet of Things (IoT) has led to the design and
incorporation of innovative user control mechanisms, one category based on
derived biometric data and known as Brain Control Interface (BCI). BCI
s in EEG and allow users to interact with
computerised systems, such as the Industrial Internet of Things (IIoT),
intuitively. However, the utilisation of EEG data in the IIoT for actuator control
olicing the IIoT
introduces considerable implications for the users’ cognitive privacy. Thus,
considering the importance of cognitive privacy in an evolving era of smart
environments, it becomes imperative to develop methods that can ensure the
rivacy of users. Cognitive privacy also protects the anonymity of
2. corporations and law enforcement. Furthermore, it maintains the inherent
information found in EEG measurements, used by various machine and deep
learning models. This paper proposes a novel deep learning-based human
cognitive privacy framework, named DeepCog, that ensures users’ privacy
through the application of feature transforming normalisation. A deep MLP
model then processes the encoded data to classify samples according to an
integer-based subject ID, enabling the framework to select the correct
secondary deep MLP model (one for each subject) to identify eye activity. Our
experiments indicate high accuracy of 93.4%, with precision 93.2% and recall
93.8%, outperforming compelling techniques.