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.