Colloquium Artificial Intelligence & Computer Science - Pinar Yolum Birbil, University of Utrecht

Title: Situation-Based Privacy Decisions: Privacy Assistants Working with Humans

Abstract:
Privacy on the Web is typically managed by giving consent to individual Websites for various aspects of data usage. This paradigm requires too much human effort and thus is impractical for Internet of Things (IoT) applications where humans interact with many new devices on a daily basis. Ideally, handling of privacy has to be reasoned by software privacy assistants, depending on the norms, context, as well as the trust among entities. Thus, the privacy assistants can help by making privacy decisions in different situations on behalf of the users. To realize this, we propose an agent-based model for a privacy assistant. The model identifies the contexts that a situation implies and computes the trustworthiness of these contexts. Contrary to traditional trust models that capture trust in an entity by observing large number of interactions, our proposed model can assess the trustworthiness even if the user has not interacted with the particular device before. Moreover, our model can decide which situations are inherently ambiguous and thus can request the human to make the decision. We show the applicability of our model using a real-life data set as well as adjustments that are needed to serve different types of users.