Enabling the automated recognition and sharing of a user's context is a primary motivation for many pervasive computing applications. In the past, a significant amount of research has been focusing on the aspect of effective and efficient recognition. Yet, when context is shared with others, the resulting disclosure of personal information can have undesirable privacy implications. A common solution to this problem is the manual creation of an application-specific privacy policy that defines which information may be shared with whom. However, as the number of applications increases, such a manual approach becomes increasingly cumbersome and over time, it is likely to lead to incomplete or even inconsistent policies. In this paper, we discuss how a privacy policy can be derived automatically by analyzing the user's sharing behaviour when using online collaboration tools. Our approach retrieves shared content and the associated sharing settings, detects context types and automatically derives a privacy policy that reflects the user's past sharing behaviour. To validate our approach, we have implemented it as an extensible software library for the Android platform and we have developed plug-ins for two popular collaboration tools, namely Google Calendar and Facebook.

Online collaboration tools such as Google+, Face-book or Dropbox have become an important and ubiquitous mediator of many human interactions. In the virtual world, they enable secure interaction by controlling access to shared resources. Yet relying on them to support synchronous direct interactions, such as face-to-face meetings, might be suboptimal as they require reliable online connectivity and even then often introduce delays. A much more efficient way of co-located resource sharing is the use of local communications, such as ad-hoc WiFi. Yet setting up the necessary encryption and authentication mechanisms is often cumbersome. In this paper, we present PIKE, a key exchange protocol that minimizes this configuration effort. PIKE piggybacks the exchange of keys on top of an existing service infrastructure. To support encryption or authentication without Internet connection, PIKE relies on triggers for upcoming personal interactions and exchanges keys before they take place. To evaluate PIKE, we present two example applications and we perform an experimental as well as an analytical analysis of its characteristics. The evaluation indicates that PIKE is broadly applicable, scales well enough to support larger events and provides a level of security that is (at least) comparable to the one provided by the underlying service.

One of the key characteristics of pervasive computing applications is their ability to adapt to their environment. One possible approach to simplify the development of such adaptive applications is to use middleware to automate adaptation decisions. However, in many application scenarios, adaptation decisions can have critical security implications. Thus, when designing middleware to support adaptation it is necessary to take security constraints into account. In previous work, we presented generic role assignment as a middleware abstraction to support adaptation. In contrast to other approaches, role assignment enables adaptation within a single and across multiple smart environments in a flexible, application-overarching manner. In this paper, we extend the concept to support security constraints. Furthermore, we describe the necessary modifications to our existing role assignment system. Finally, we evaluate our approach showing resulting benefits and limitations. In comparison with existing approaches, the proposed approach is more flexible and does not introduce a single point of trust.

Existing context recognition applications for personal mobile devices are usually fine-tuned to recognize the set of characteristics required to support a particular user task. Outside of a laboratory environment, however, users are often involved in multiple tasks at a time which requires the simultaneous execution of several applications. Yet, due to the energy constraints of most personal mobile devices this approach is inherently limited in scale. In this paper, we show how this problem can be avoided by applying a component-based approach to application development and execution. We present a component system that enables developers to build individual applications in isolation without compromising runtime efficiency. When applications are executed simultaneously, the component system applies a novel technique called configuration folding to automatically remove redundancies. Our experimental evaluation of configuration folding shows that it can save up to 48 percent of energy when applied to a music and a speech detection application, thus, amortizing energy costs after a few seconds of execution.

Due to the fact that distant objects are often less relevant to an application than objects in the proximity, middleware systems for pervasive computing typically exploit locality to improve efficiency. To do this, they configure the environment by introducing logical boundaries that reduce the number of interacting devices. Yet, in cases where applications require the interaction with distant objects, the boundaries become an artificial barrier that must be overcome by supplemental mechanisms. In this paper, we show how this problem can be avoided by using role assignment as a generic mechanism for environment configuration. To do this, we first derive the requirements for configuring a pervasive computing environment. We discuss how these requirements can be met by means of role assignment. To evaluate the approach, we present a prototypical implementation which we use to quantify the resulting overheads. The results indicate that role assignment enables a more flexible definition of boundaries at a low cost.