Activity inference is an active area of ubiquitous computing research. By training machine learning algorithms on data from sensors worn by volunteers, researchers hope to develop software that can interact more naturally with the user by inferring what the user is doing. In this thesis, we use the same sensor data to infer which volunteer is carrying the sensors. Such inference could be useful -- for example, a mobile device might infer who is carrying it and adapt to that user's preferences. It also raises some privacy concerns, since an attacker could learn more about a user by linking together several sensor traces from the same user. We develop a model to differentiate users based on their sensor data, and examine its accuracy as well as the potential benefits and pitfalls.