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, software 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 evaluate various aspects of the model
using a real-life data set and report adjustments that
are needed to serve different types of users well.
