Design of collaborative and context aware mobile applications considering normative requirements from legal science and computer science (NORA)

Context-aware applications support users with additional services and functionalities in a situational manner and based, of course on their context. Mobile devices play a vital role in such scenarios, allowing for gathering and processing of information via physical and physiological sensors, taken directly from users’ environments. Context-aware applications, for example, can obtain users’ current locations, activities, as well as their emotions and stress levels. Collaborative context-aware applications gather and combine data of multiple users improving the recognition and prediction of such information, commonly realized via machine learning. By using additional entities, new routines, and information on people’s behaviour can be extracted. Such collaborative processing of information, unfortunately, poses a significant risk to privacy, as sensitive and person-related information is processed on external entities. The goal of this project is, therefore, to design collaborative context-aware and mobile applications as well as machine learning algorithms while considering normative requirements of the law and computer science. By using the method for concretizing normative requirements (in German: Methode zur Konkretisierung Normativer Anforderungen (KONA)) legal as well as technical criteria, requirements, and designs will be derived. In the course of the project, normative requirements will be further extended to apply to concrete technical designs, addressing legal (e.g., data scarcity and transparency) as well as technical norms and criteria (e.g., recognition accuracy and processing times). This still-to-be-developed method KONA will be evaluated on two mobile collaborative context-aware applications as well as machine learning algorithms to recognize and predict contextual information.

上下文感知的应用程序当然以情境方式和基于其上下文的方式为用户提供其他服务和功能。移动设备在这种情况下起着至关重要的作用，可以直接从用户的环境中采取物理和物理传感器来收集和处理信息。例如，上下文感知的应用程序可以获取用户的当前位置，活动以及他们的情绪和压力水平。协作上下文感知的应用程序收集并结合了多个用户的数据，以改善这些信息的识别和预测，通常通过机器学习实现。通过使用其他实体，可以提取有关人们行为的新例程和信息。不幸的是，这种信息的协作处理对隐私构成了重大风险，因为对外部实体进行了敏感和与人有关的信息。因此，该项目的目的是设计协作环境感知和移动应用程序以及机器学习算法，同时考虑法律和计算机科学的正常要求。通过使用该方法来具体化正常要求（在德语中：Zur Konkretisierung Normativer Anforderungen（Kona））法律以及技术标准，要求和设计将得出。在项目过程中，正常要求将进一步扩展到具体技术设计，解决法律（例如，数据稀缺和透明度）以及技术规范和标准（例如，识别准确性和处理时间）。这种仍在开发的方法KONA将在两个移动协作上下文感知应用程序以及机器学习算法上进行评估，以识别和预测上下文信息。