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