Challenges in Supporting End-User
Privacy and Security
Management with Social Navigation
Jeremy Goecks, W. Keith Edwards,
and Elizabeth D. Mynatt
Decision Making in End-User Privacy
and Security Management
Privacy and security management often about boundary
management (Palen and Dourish, 2003; Dourish et al. 200?)
» make decisions about what to allow across boundary
» “hard on the outside, soft on the inside”
» often cannot automate privacy and security management
(Edwards et al., 2007)
Users must make decisions, but:
» minimal knowledge
» low motivation
» prefer to delegate (Dourish et al. 200?)
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Social Navigation
Collects, aggregates, and displays community data
Many kinds of community data
» actions, choices
» tags
» free text
Supports socially-aided choices and decision making
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Matching Social Nav to DecisionMaking Problems
Problem
How Social Navigation Helps
technical information
difficult to understand
lack of motivation
social information easier to understand
also (Herzog & Shahmehri, 2007 )
easy to integrate and use
also (DiGioia & Dourish, 2005 )
preference for delegation
enables delegation
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Outline
Introduction
Acumen and Bonfire
Challenges
Summary
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Acumen
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Acumen
Mavens (experts) data shown
inside of community data
» promote good herding
Automatic cookie management
via rules
» balance automation with control
» make clear whether cookies
blocked manually or via rule
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Reflections on Acumen
Approach
» support for “in the moment” decision making for privacy
management with social navigation
» promote good herding, mitigate bad herding
Preliminary deployment: 9 people, 6 weeks, ~2650 websites
Observations
» difficult to evaluate decisions
multiple information sources
» herding behavior observed; difficult to manage
experts not trusted, so not possible to promote “good” herding
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Bonfire
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Bonfire
Approach
» mitigate herding by answering “what”
and “why”
» complementary sources of social
navigation data
Lessons Learned
» providing “what” and “why” makes it
easier to use community data
» cost of wrong answers is high, so
preventing wrong answers is critical
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Outline
Introduction
Acumen and Bonfire
Challenges
Summary
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Challenges Raised by Acumen &
Bonfire
Supporting use of multiple information sources for
decision making
» information that individual already has
» community data
Managing herding
» general lack of knowledge & ranges of expertise
» desire to delegate
» undesirable feedback loop that can lead to false majority
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Traditional & Non-Traditional,
Incomplete Information
Social navigation traditionally applied to decisions
for books, movies, music
» information is relatively complete
» clear grasp of personal preferences
Privacy and many security decisions often different
» use of incomplete and potentially inaccurate information
» potentially unclear grasp of personal preferences
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Model for Decision Making with Social
Navigation
My Choice!
1. go with observations,
knowledge, or facts
2. go with community
consensus
Preferences,
Incentives,
Biases
Observations,
knowledge, facts
Community
data
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Informational Cascades
Informational cascades - herding that arises when people
ignoring their own information and go with the community
(Banerjee 1992; Bikhchandani, Hirshleifer et al. 1992)
Lack of Info
or Uncertainty
Real-world examples: financial markets (Devenow and Welch 1996; Walden and
Browne 2002), movies & fads (Walden and Browne 2002), IT adoption (Bikhchandani, S.,
D. Hirshleifer, et al. 1998), politics (Bartels 1988), medicine (Robin 1984, Taubes 2007)
Follow
Misrepresentation
Majority
of Info
“False” Majority
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Informational Cascades in Social
Navigation Systems
Social nav systems meet cascades criteria
» sequential decision making
» can see what others have done
» discrete set of choices
Cascade behavior has been shown to occur in social nav
systems (Goecks 2009)
Cascades occur regardless of system functionality
» CF vs. simple aggregation
» activity data collection vs. ratings
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Info Cascades + Social Nav + Privacy
and Security Management
Cascades are especially likely in social nav systems
for privacy and security management
Because
» general lack of expertise & knowledge
» limited interest in providing additional data
» desire to delegate
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Mitigation via Algorithms
Methods for
» limiting malicious ratings in a social nav system (Resnick and Sami, 2007)
» starting cascades (Domingos & Richardson, 2001)
» identifying cascades (Leskovec et al., 2007)
Challenges
» unclear how to mitigate cascades
cascades often started by accident, different people
» substantial data required for modeling
early adopters hurt
users must maintain stable identities
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Mitigation via UI Techniques
Balance competing goals
» use of community data
» capture user knowledge and expertise
Implicit/Explicit
User Burden
Aggregation
Expressiveness
Activity Data
Implicit
Low
Easy
Low
Ratings
Explicit
Low-Moderate
Easy
Low-Moderate
Free Text
Explicit
High
Hard
High
Tagging
Explicit
Moderate
Moderate
Moderate-High
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Future Work
Quantifying cascades impact
» how often do cascades occur?
» what is the cost of a “bad” decision? of many “bad” decisions?
true, false positives / true, false negatives
Bridging social navigation research with crowd wisdom
research
» when is a choice considered “correct”?
» when can a choice be automatically acted on?
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Summary
Acumen and Bonfire demonstrate how simple social navigation
systems can be applied to privacy and security management activities
Info cascades can be quite problematic for social nav systems applied
to privacy and security management
» much use of incomplete, inaccurate information
» much potential herding
Going forward
» mitigating cascades via algorithms & user interaction
» understanding the prevalence and cost of cascades
» when to automatically act on community data
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Thanks!
Contact Information
http://www.cc.gatech.edu/~jeremy
jeremy@cc.gatech.edu
Jeremy Goecks
Everyday Computing Lab & GVU Center
School of Interactive Computing, College of Computing
Georgia Institute of Technology
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Understanding Individual & Group
Behavior in Social Nav Systems
Social Influence
Individual
Normative
Influence
Informational
Influence
Herding
Group
Irrational
Herding
Informational
Cascades
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