Regulations for privacy protection aim to protect individuals from the unauthorized storage, processing, and transfer of their personal data but oftentimes fail in providing helpful support for understanding these regulations. To better communicate privacy policies for smartwatches, we need an in-depth understanding of their concepts and provide better ways to enable developers to integrate them when engineering systems. Up to now, no conceptual model exists covering privacy statements from different smartwatch manufacturers that is reusable for developers. This paper introduces such a conceptual model for privacy policies of smartwatches and shows its use in a model-driven software engineering approach to create a platform for data visualization of wearable privacy policies from different smartwatch manufacturers. We have analyzed the privacy policies of various manufacturers and extracted the relevant concepts. Moreover, we have checked the model with lawyers for its correctness, instantiated it with concrete data, and used it in a model-driven software engineering approach to create a platform for data visualization. This reusable privacy policy model can enable developers to easily represent privacy policies in their systems. This provides a foundation for more structured and understandable privacy policies which, in the long run, can increase the data sovereignty of application users.

The concept of digital shadows helps to move from handling large amounts of heterogeneous data in production to the handling of task- and context-dependent aggregated data sets supporting a specific purpose. Current research lacks further investigations of characteristics digital shadows may have when they are applied to different levels of the automation pyramid. Within this paper, we describe the application of the digital shadow concept for two use cases in injection molding , namely geometry-dependent process configuration , and optimal production planning of jobs on an injection molding machine. In detail, we describe the creation process of digital shadows, relevant data needs for the specific purpose, as well as relevant models. Based on their usage, we describe specifics of their characteristics and discuss commonalities and differences. These aspects can be taken into account when creating digital shadows for further use cases.