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Many personal informatics applications integrate social sharing of sensed activity data. Sharing can promote personal goals through social mechanisms such as encouragement and competition. However, fine-grained sharing can often allow people to infer the context of sensed activity. This can surface significant privacy concerns, as people may unintentionally disclose aspects of their everyday life unrelated to their motivations for sharing. We first discuss scenarios that motivate fine-grained sharing but are restricted by current all-or-nothing approaches. We then discuss our interests in exploring potential alternative approaches to sharing fine-grained personal activity data.

Created by Ian Li. HCII, Carnegie Mellon University.