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The proliferation of ubiquitous computing introduces several challenges to user privacy. Data from multiple sensors and users is aggregated at various scales to produce new, fine-grained inferences about people. Users of these systems are asked to consent to sharing their data without full knowledge of what data are recorded, how the data are used, who has access to the data, and most importantly risks associated with sharing. Recent work has shown that provoking privacy speculation among system users, by visualizing these various aspects, improves user knowledge and enables them to make informed decisions about their data. This paper presents a conceptual model of how researchers can make inferences that provoke privacy speculation among system users and a case study applying the model.