Privacy management in smart environments

Smart environments are physical spaces, enriched with various sensors, devices, and services which aim to support inhabitants in their activities. A typical application area are environments which support collaboration, enhance productivity, or boost creative activities – for instance smart meeting rooms. User adapted behavior and personalization are central themes of smart environments. However, next to improving the overall user experience these features and their underlying technologies are a rich source of privacy issues. Privacy is a broad term which is used for different concepts. Especially in collaboration scenarios the concept of interpersonal privacy plays an important role. It affects the communication of personal information within social interactions and aims to support social norms and boundaries. The manifold and pervasive communication modalities in smart environments easily may disrupt social interactions instead of supporting them. In contrast to privacy preferences in face of abstract entities like governments or corporations, information disclosure decisions in social interactions tend to be more dynamic and intuitive, rather than static and rational, which results in more complex disclosure parameters whose values are harder to predict and often not easy to describe formally. Existing solutions to protect and manage privacy in smart environments do not sufficiently consider the specific characteristics of interpersonal privacy within smart environments. In order to objectively address the generally vague concept of interpersonal privacy, this work introduces a model to express disclosure decisions in social interactions within smart environments. The model is used to develop several set-based patterns of privacy management which make it possible to programmatically manage interpersonal information exchange. The practical relevance of these patterns is evaluated in a corresponding user study. As there is no sole disclosure control method which allows intuitive and individual privacy control, this work further presents a reference model of a composite disclosure control system, which stages and integrates various control methods. The benefit of this system is twofold. First, single components complement each other, and second, users are able to practice privacy according to their individual needs and preferences. One specific control method, predicting information disclosure with the help of machine learning techniques, is addressed in detail. Several existing learning methods as well as a novel one, based on the previously developed privacy patterns, are presented and evaluated. Additionally, patternbased prediction validators are developed which reduce the impact of wrong predictions. Evaluation results show that learning methods are able to correctly predict disclosures in many cases while still providing workload-reducing suggestions in cases where predictions are not correct. The novel learning method is a competitive alternative to existing methods with special properties especially useful for interpersonal privacy control. This work is completed with general guidelines how to support interpersonal privacy when designing and engineering smart environments.