Seminar/Thesis: Automated Permission Preference Manager
Privacy and functionality are usually considered at odds with each other. As data privacy practices are enforced, data becomes a scarce resource. In order to balance the protection of user privacy with functional services, the European Union enforced the general data protection regulation (). Inspired by legal principles, GDPR is the framework where users, controllers and data processors rights and responsibilities are specified.
Out of the six mechanisms that the GDPR establishes to allow data controllers to capture subject’s data, consent is the simplest to implement . As a result, applications have often opted for asking consent as means to make the user aware of their needs for processing data. However, consent is the weakest mechanism to justify gathering and processing of user data, since consent can be removed at any time and the user’s data has to be erased.
As mobile and web-based applications request consent, the burden is on the user to assess and mitigate security risks and enforce privacy preference . As the number of applications users interact with increases, the problem becomes intractable, especially when considering users with cognitive impairments, or with no technology familiarity. The problem becomes critical in the digital health realm when applications are suggested/imposed on users by third parties. For example, physicians and insurance companies suggest elderly citizens to use nutrition tracking applications.
How can we create a representation of user privacy preferences that can be used to automate the navigation and data exchange negotiation of Digital Health services?
- Gain an overview of full-stack development for medical health applications
- Explore and understand the social and technical challenges and implications of automated permission preference manager
- Apply machine learning models to permission preference
|Period||Winter Semester 2021/22|
|Presence time||Virtual seminar, working from remote|
|Useful knowledge||Python, data analytics, Android programming|
|Work distribution||30% development, 20% experimentation, 30% data analysis and evaluation, 10% consultation, 10% reporting|
|Med. Eng. designation||Advanced Context Recognition (ACR)|
|StudOn link||Coming soon|
|First meeting||Coming soon|
|Registration||Via StudOn, obligatory after introduction.|
Additional up-to-date literature recommendations are provided during the meeting sessions.
- Final project presentation, demonstrator and final report.