AbstractsComputer Science

Ubiquitous computing is characterized by the integration of computing aspects into the physical environment. Physical objects gain digital sensing, processing, and communication capabilities. This introduces a number of privacy challenges. Smart devices may gather and exchange information about users with remote parties anywhere in the world, while the complexity of ubiquitous computing systems makes it difficult for users to accurately estimate privacy implications. We propose a dynamic privacy adaptation process that leverages context awareness to support users in their privacy regulation activities. Our user-centric privacy context model captures privacy-relevant contextual information in a given situation. Privacy-relevant context changes trigger our privacy decision engine, which employs case-based reasoning and context-based preference rules to reason about the user"s privacy preferences. Privacy preferences then have to be realized and implemented in diverse ubiquitous computing environments. We analyze what factors influence the selection of suitable realization strategies, and we provide an overview of common optimistic and pessimistic privacy control strategies. In particular, we highlight our contributions to an architecture for distributed privacy policy enforcement and outline a conceptual approach for combining the proposed privacy adaptation process with multimodal interaction systems. To evaluate our approach, we conducted an in-depth case study with a privacy-adaptive calendar display that implements the dynamic privacy adaption process. Ten participants used the developed system for three weeks with their own calendar data in a real work setting. Our results indicate that dynamic privacy adaptation is a feasible approach for supporting users in the regulation of their privacy in complex ubiquitous computing environments.