Managing the Privacy of Incidental Information During Collaboration Kirstie Hawkey and Kori Inkpen Dalhousie University {hawkey, inkpen}@cs.dal.ca Concept Challenges Ad hoc Collaboration Colleagues often gather around a computer Incidental Information Many traces of past activities are visible with casual inspection. Complexity Reduction Large volume of information. Multiple contexts of creation and viewing of incidental information. Must balance amount of control with maintenance time and effort. Privacy patterns suggest a semi-automated approach is feasible. Data Analyses High volume of logged data during field studies. Numerical averages insufficient due to individual differences. Data mining and visualization techniques may uncover patterns. System Evaluation Field setting required for evaluation of management solutions. To encourage natural behaviour, users need to maintain privacy. Large volume make fine-grained self-reports tedious. Privacy Patterns Patterns on a per-window basis suggest a semi-automated approach to privacy management may be feasible: Individual Behaviours Privacy concerns and personal information management styles are very individual. Privacy Normative privacy for personal displays does not apply; the display is an object in the collaboration. Rapid Bursts of Browsing Large Volume of Pages Personal Information Management Managing large amounts of personal information is complex. Sequential Representation Streaks Transitions Field Study: Privacy Gradients Next Steps Temporal Representation Temporal Patterns Window Re-visitation Potential Solutions Browser windows of differing privacy levels? Filter which incidental information is displayed. Classify new information as it is generated. Research studies Survey of privacy concerns with respect to incidental information (underway). Field study examining privacy in context of location and pages (underway). Longitudinal field study of management solution Methodology Field study for one week. 20 laptop users (multiple contexts). Client-side logging. Electronic diary. Privacy gradients. Paper classification tasks. Goodness of Fit 75% of gradients fit most of the time; 20% fit all of the time Hard to classify: Sites with multiple purposes Sites with variable content Overall Privacy Gradient Usage Patterns Clusters C1 C2 C3 C4 Privacy Gradient Overall Final Cluster Centers Public 42% 22% 36% 62% 18% Semi-Public 25% 58% 21% 16% 28% Private 15% 9% 11% Don’t Save 7% 46% Number of Participants 3 5 10 2 Acknowledgements Thanks to the members of the EDGE Lab for their support. This research is funded in part by NSERC