The pervasive nature of future living environments, saturated with sensors and context-detecting services, pose a completely new challenge for computer science: the art of virtual disappearance. In many situations individuals do not want to be tracked by the environment and do not want their whereabouts to be known publicly or even by their friends and relatives. Today's technology often allows us to use white lies in such circumstances. The question we pose in this paper is: Can we achieve the same using pervasive computing technologies? In this paper we show how our User-centric Privacy Framework can be extended to allow users to pro-actively use white lies as a means to disguise their location or activity without sacrificing the use of context-services as a whole. As a result we are confident that also in the future we can perform some magic: disappearing for a while -when needed.

One inherent feature of pervasive computing environments is the need to gather and process context information about real persons. Unfortunately, this unavoidably affects persons' privacy to a large degree. Each time today a citizen uses his cellular phone, his credit card or surf the web, he is leaving a trace that is stored for some reason. In a pervasive sensing environment, however, the amount of information collected is a) much larger than today and b) might be used to reconstruct personal information with great accuracy. The question we address in this paper is to control dissemination and flow of personal data across organizational, as well as personal boundaries, i.e., to potential addressees of privacy relevant information. This paper presents the User-Centric Privacy Framework (UCPF). It aims at protecting a user's privacy based on the enforcement of privacy preferences. They are expressed as a set of constraints over some set of context information. To achieve the goal of cross-boundary control, we introduce two novel abstractions, namely Transformations and Foreign Constraints, in order to extend the possibilities of a user to describe his privacy protection criteria beyond the current expressiveness ussually found today. Transformations are understood as any process that the user may define over a specific piece of context. This is a main building block for obfuscating or even plainly lie about the context in question. Foreign Constraints are an important complementing extension because they allow for modeling conditions defined on external users that are not the tracked individual, but may influence disclosure of personal data to third parties. We are confident that these two easy-to-use abstractions together with the general privacy framework presented in this paper constitute a strong contribution to the protection of the personal privacy in pervasive computing environments.

Abstract-The increasing traffic and the increasing number of sensors both in cars and in the infrastructure pose new challenges but also create new opportunities for traffic control. If the sensor data in various states of interpretation and aggregation could be shared and reused, it would be possible to minimize accidents and improve the traffic situation. In this paper we describe an approach to automatically configure sensor data fusion systems across the boundaries of independent subsystems, where information on all levels can be exchanged. The basis for this is a formal description of all required meta-information that enables the reasoning for automatic configuration.

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