Privacy Wizards for Social Networking Sites
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Transcript Privacy Wizards for Social Networking Sites
Privacy Wizards
for Social
Networking
Sites
Reporter :鄭志欣
Advisor: Hsing-Kuo Pao
Date : 2011/01/17
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Reference
Lujun Fang and Kristen LeFevre. "Privacy
Wizards for Social Networking Sites." 19th
International World Wide Web Conference
(WWW2010,Best student paper).
Lujun Fang, Heedo Kim, Kristen LeFevre, Aaron
Tami ,"A Privacy Recommendation Wizard for
Users of Social Networking Sites" 17th ACM
conference on Computer and
communications security (ACM
CCS2010,Demo).
www.eecs.umich.edu/dm10/slides/fang.pptx
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Outline
Introduction
Wizard
Overview
Active Learning Wizard
Evaluation
Conclusion
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Introduction
Social
network sites have been
increasingly gaining popularity.
More than 500 million members
Privacy
is a huge problem for users of
social networking sites.
More Personal information
A lot of Friends (Ex: FB average 130)
Facebook’s
“Privacy Setting” is too detail.
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Goal
We
propose the first privacy wizard for
social networking sites.
The goal of the wizard is to automatically
configure a user's privacy settings with
effort from the user.
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Challenges
Low
Effort , High Accuracy
Graceful Degradation
Visible Data
Incrementality
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Idea
Idea:
With limited
information,
build a model to
predict user’s
preferences,
auto-configure
settings
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Wizard Overview
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Active Learning Wizard
Classifier
Each friend as a feature vector
Question
How to extract features from friends?
How to solicit user input?
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Extracting
Features
Age Sex G G
G
G
G
G
0
1
2
20
21
22
G3
Obama Pref. Label
Fan
(DOB)
(Alice) 25
F
0
1
0
0
0
0
0
1
allow
(Bob) 18
M
0
0
1
1
0
0
0
0
deny
(Carol) 30
F
1
0
0
0
0
0
0
0
?
G0
G1
G21
G2
G3
G20
G22
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Soliciting User Input
Ask
Simple and Right questions
Question
:
Would you like to share your Date of Birth
with ?
How
to choose informative friends using
an active learning approach?
Uncertainty sampling
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Figure 5: Screenshot of user study application , general questions
Figure 6: Screenshot of user study application,detailed questions.
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Evaluation
Gathered raw preference data from 45
real Facebook users.
How effective is the active learning wizard,
compared to alternative tools?
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Experiments
DTree-Active
Model is a Decision tree
Uncertainty sampling
Decision
Tree
Model is a Decision tree
User labels randomly selected examples
Brute-Force
Like Facebook policy-specification tool
Assign friends to lists
Result
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Tradeoff
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Conclusion
Privacy
is an important emerging problem
in online social networks.
This paper presented a template for the
design of a privacy wizard, which
removes much of the burden from
individual users.