CS Other Presentations

Department of Computer Science - University of Cyprus

Besides Colloquiums, the Department of Computer Science at the University of Cyprus also holds Other Presentations (Research Seminars, PhD Defenses, Short Term Courses, Demonstrations, etc.). These presentations are given by scientists who aim to present preliminary results of their research work and/or other technical material. Other Presentations serve as a forum for educating Computer Science students and related announcements are disseminated to the Department of Computer Science (i.e., the csall list):
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Presentations Coordinator: Demetris Zeinalipour

PhD Defense: Towards Privacy-Aware Usage of Fitness Trackers and Smart Home Devices: Enhancing User Awareness in the GDPR Era, Mrs. Alexia Dini (University of Cyprus, Cyprus), Friday, June 9, 2023, 10.00-11.00 EEST.

The Department of Computer Science at the University of Cyprus cordially invites you to the PhD Defense entitled:

Towards Privacy-Aware Usage of Fitness Trackers and Smart Home Devices: Enhancing User Awareness in the GDPR Era

Speaker: Mrs. Alexia Dini
Affiliation: University of Cyprus, Cyprus
Category: PhD Defense
Location: Room 023, Faculty of Pure and Applied Sciences (FST-01), 1 University Avenue, 2109 Nicosia, Cyprus (directions)
Date: Friday, June 9, 2023
Time: 10.00-11.00 EEST
Host: Prof. George Papadopoulos (george-AT-cs.ucy.ac.cy)
URL: https://www.cs.ucy.ac.cy/colloquium/presentations.php?speaker=cs.ucy.pres.2023.dini

The enormous amount of data collected and shared among smart home devices and fitness trackers has raised a serious issue regarding the privacy and the awareness of the users about how their data are collected and shared. These data may become available to interested third parties, who can process them with the intention to derive further knowledge and generate new insights and inferences about the users. Inferences have become one of the the biggest threats to privacy, as they compromise a basic privacy law, which is to allow a person to control who knows what about them. These issues put the user privacy at risk due to the inferences threat and the lack of user awareness about these inferences. Despite the research interest in the development of privacy-preserving methods to address the privacy challenges in IoT, the exploration of the user’s perspective and needs have not been adequately addressed in the effort to provide user-centric privacy-preserving solutions in IoT. This is the gap that this doctoral thesis attempts to address, aiming to comprehend smart home devices and fitness trackers users’ awareness over their data and privacy and to develop mechanisms to evaluate and increase their awareness and empower them with control over their data and privacy in this context. In more detail, it presents the characteristics that a user-centric privacy-preserving framework in IoT should own and introduces a conceptual framework based on these characteristics that demonstrates how the users can be provided with the functionalities and the tools needed to be in control of their personal data. The results of experiments through the application of machine learning techniques using real datasets that have been created during this research through specific scenarios, provide insights into the types of inferences that can be made from smart home and fitness tracker data, and highlight the importance of making the user aware about them. For this reason, we contribute with a privacy tool, “PrivacyEnhAction”, that aims to increase the user awareness about potential privacy vulnerabilities that emerge from the use of these devices. A qualitative user study was conducted to evaluate the impact of PrivacyEnhAction to the awareness of the participants regarding the possible inferences that can be obtained from their fitness tracker data, with positive results. To further assist in the effort of increasing the awareness of the users in this context, this thesis provides a methodology for the analysis of the text of fitness trackers and smart home devices privacy policies, which is also implemented in the PrivacyEnhAction web application.

Short Bio:
Alexia Dini is a Ph.D. candidate at the Department of Computer Science of the University of Cyprus. She received her MSc in Software Engineering from the University of Bradford, UK (1999), and her BSc in Computer Science from the University of Kent at Canterbury, UK (1998). She has 24 years of industrial experience in the software development and education sector in Cyprus and the UK. Her research interests include Privacy in IoT, User Privacy, Connected Devices, Privacy Preservation for IoT, User Privacy-Awareness, Data Analytics and Machine Learning.

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