privacy wizards for social networking sites reporter : 鄭志欣 advisor: hsing-kuo pao date :...
TRANSCRIPT
Privacy Wizards for Social Networking Sites
Reporter :鄭志欣Advisor: Hsing-Kuo Pao
Date : 2011/01/17
1
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
2
Outline Introduction Wizard Overview Active Learning Wizard Evaluation Conclusion
3
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.
4
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.
5
Challenges Low Effort , High Accuracy Graceful Degradation Visible Data Incrementality
6
Idea
7
Idea: Idea: With limited information, build a model to predict user’s preferences, auto-configure settings
Wizard Overview
8
Active Learning Wizard Classifier
Each friend as a feature vector
Question How to extract features from friends? How to solicit user input?
9
Extracting Features
10
Age Sex G0 G1 G2 G20 G21 G22 G3
ObamaFan
Pref. Label(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 ?
G0G1
G2
G3
G20
G21
G22
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
11
12
Figure 5: Screenshot of user study application , general questions
Figure 6: Screenshot of user study application,detailed questions.
13
Evaluation Gathered raw preference data from 45
real Facebook users. How effective is the active learning
wizard, compared to alternative tools?
14
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
15
Result 16
Tradeoff
17
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.
18