Autonomous systems have progressed from theory to application especially in the last decade, thanks to the recent technological evolution. The number of autonomous systems in our surroundings is increasing rapidly. Since these systems in most cases handle privacy-sensitive data, the privacy concerns are also increasing at a similar rate. However, privacy research has not been in sync with these developments. Moreover, the systems are heterogeneous in nature and continuously evolving which makes the privacy problem even more challenging. The domain poses some unique privacy challenges which are not always possible to solve using existing solutions from other related fields. In this thesis, we identify open privacy challenges of autonomous systems and later propose solutions to some of the most prominent challenges. We investigate the privacy challenges in the context of smart home-based systems including Ambient Assisted Living (AAL) systems as well as autonomous vehicles. In the case of smart home, we propose a framework to enhance the privacy of owners during ownership change of IoT devices and conduct a systematic literature review to identify the privacy challenges of home-based health monitoring systems. For autonomous vehicles, we quantify, improve, and tune the privacy utility trade-off of the image de-identification process. Our investigation reveals that there is a lack of consideration when it comes to the privacy of autonomous systems and there are several open research questions in the domain regarding, for instance, privacy-preserving data management, quantification of privacy utility trade-off, and compliance with privacy laws. Since the field is evolving, this work can be seen as a step towards privacy preserving autonomous systems. The identified privacy concerns and their corresponding solutions presented in this thesis will help the research community to identify and address existing privacy concerns of autonomous systems. Solving the concerns will encourage the end-users to adopt the systems and enjoy the benefits without bothering about privacy. / <p>QC 20201116</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-285909 |
| Date | January 2020 |
| Creators | Khan, Md Sakib Nizam |
| Publisher | KTH, Teoretisk datalogi, TCS, Stockholm |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Licentiate thesis, comprehensive summary, info:eu-repo/semantics/masterThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-EECS-AVL ; 2020:63 |
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