Thesis topic:

Tool-supported privacy analysis of smart parking

• Supervisor: Mariia Bahktina
  • contact: mariia.bakhtina@ut.ee
• Motivation: The shift to data-driven decision-making brings opportunities to organisations. However, reliance on sensitive data about identifiable persons poses the obligation to cope with data privacy management concerning the local legislation. As the EU's privacy protection regulation (i.e. GDPR) has gained its power relatively recently, no established procedures or frameworks guide privacy analysis and assurance. In [1], the authors presented a tool-supported method for privacy analysis of a business process model. The proposed method aims to support the elicitation of requirements to the information system to comply with GDPR. Additionally, the method supports the selection of technical measures for privacy assurance based on their effectiveness in the context of a business process.
• Thesis aim: This study should validate the usability of the proposed [1] method by applying it to the smart parking scenario. The thesis should result in a set of privacy requirements for the scenario.
• Tasks: The tasks include using business process models (in BPMN), defining the flow of sensitive data between actors, using tools for process model analysis, requirements elicitation, and proposal privacy-enhancing technologies to meet the requirements.
• Reference:
  • [1] Bakhtina, M., Matulevičius, R., & Seeba, M. (2023). Tool-supported method for privacy analysis of a business process model. Journal of Information Security and Applications, 76, 103525. https://doi.org/10.1016/j.jisa.2023.103525

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