1. Managing the Privacy of Incidental Information During Collaboration
Kirstie Hawkey and Kori Inkpen
Dalhousie University
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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