Navigating Privacy Patterns in the Era of Robotaxis

Privacy engineering encompasses various method-ologies and tools, including privacy strategies and privacy patterns, aimed at achieving systems that inherently respect privacy. Despite the collection of numerous privacy patterns, their practical application remains under-explored. This paper investigates the applicability of privacy patterns in the context of robotaxis, a use case in the broader Mobility-as-a-Service (MaaS) ecosystem. Using the LINDDUN framework for privacy threat elicitation, we analyze existing privacy patterns to address identified privacy threats. Our findings reveal challenges in applying these patterns due to inconsis-tencies and a lack of guidance, as well as a lack of suitable privacy patterns for addressing several privacy threats. To fill the gaps, we propose ideas for new privacy patterns.