Privacy and Privacy Enhancing Technologies for Post-GDPR Ubiquitous Computing

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Privacy and Privacy Enhancing Technologies for Post-GDPR 
Ubiquitous Computing"

By

Mr. Carlos BERMEJO FERNANDEZ


Abstract

The General Data Protection Regulation (GDPR) presents a set of directives 
to give individuals control over their personal data by enforcing concrete 
actions on enterprises regarding users' privacy. However, enforcing 
actions in the context of mobile and ubiquitous computing imposes multiple 
challenges. For example, the ubiquity of smart devices, combined with the 
lack of information about the data garnered by them, hinders compliance 
with GDPR. We can observe that despite the current regulations enforced by 
the GDPR, individuals are still unaware of privacy risks when using mobile 
and ubiquitous devices such as IoT devices. Thus, a practical solution for 
increasing awareness of privacy risks and providing a useful and intuitive 
way to manage them is fundamental for safeguarding users' privacy.

This thesis presents an in-depth study of individuals' privacy from their 
conceptual models and behavior in ubiquitous computing environments. Under 
the general term of privacy, theories, and individuals' conceptual models, 
there is an underlying universal dilemma about information disclosure. 
These shared concepts regarding privacy can shed more light on the impact 
of privacy on individuals' decision-making processes in ubiquitous 
computing. Our results show that participants are firmly in favor of 
consent requests for collecting and processing personal information. 
Information disclosure should be granular, and they are not concerned 
about third parties' identity. With these underlying individuals' concepts 
in mind, we explore users' privacy-related behavior in data collection 
environments such as the web and how different interface design approaches 
(e.g., nudged) can influence individuals' choices for cookie consent 
notices. Our findings demonstrate the importance of nudged interfaces and 
the effects orthogonal nudging techniques can have on users' choices. The 
aforementioned results allow us further to explore users' privacy-related 
behavior in smart device ecosystems. We study the effects of contextual 
information visualization on users' privacy perceptions. Our results show 
new insights into factors mediating user's privacy perceptions and provide 
design guidelines for improving users' knowledge of risks associated with 
smart devices using AR-based privacy assistants. These findings guide us 
to propose a privacy-preserving assistant driven by augmented reality (AR) 
for smart devices at home, namely Privacy Augmented Reality Assistant 
(PARA). PARA owns two key functions of contextualizing data disclosure and 
configuring privacy settings in the appropriate usage context. Our results 
show that PARA increases users' privacy perceptions with a higher 
intention of applying privacy protection mechanisms. PARA serves as an 
intuitive yet informative privacy assistant for smart device ecosystems. 
Finally, we propose a system that preserves shoppers' privacy in retail 
analytics. EyeShopper is an innovative system that tracks shoppers' gaze 
when facing away from the camera and provides insights into their physical 
stores' behavior. The lack of facial features (i.e., identifiable 
information) in EyeShopper can open new approaches in retail analytics 
while providing privacy protection according to the GDPR.


Date:			Wednesday, 16 June 2021

Time:			10:00am - 12:00noon

Zoom Meeting: 
https://hkust.zoom.us/j/95506987395?pwd=MDBxakdISTl1Q3V1Qzd1L1BxeDBjdz09

Chairperson:		Prof. Hai YANG (CIVL)

Committee Members:	Prof. Pan HUI (Supervisor)
 			Prof. Shing-Chi CHEUNG
 			Prof. Dimitris PAPADOPOULOS
 			Prof. Shenghui SONG (ISD)
 			Prof. Dali KAAFAR (Macquarie University)


**** ALL are Welcome ****