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A Network-Based, Multidisciplinary Approach to Intention Inference

The ability to successfully infer private behavioral intentions using publicly-available digital records has a far-reaching impact. Unlike other private attributes such as demographic attributes, an intention oftentimes leads to a near-future behavior. Prior knowledge about such future behaviors can be seen as “actionable intelligence” and constitutes a significantly bigger risk to users’ privacy than knowledge of non-behavioral attributes. In this paper, we present a novel, multidisciplinary methodology for behavioral-intention inference. Using Bayesian-Networks, we model a behavioral intention using a set of causes that influence the intention’s formation, a set of effects that are caused by the intention, and various dependency relations within and between those sets. Unlike the methodologies used in prior attribute-inference works, which are oftentimes tailored to a single target attribute, our methods can be applied to different types of intentions from a diverse set of domains as we demonstrate by applying our model to multiple real-world intention-inference tasks.