privacy wizards for social networking sites reporter : 鄭志欣 advisor: hsing-kuo pao date :...

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

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Idea: Idea: With limited information, build a model to predict user’s preferences, auto-configure settings

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

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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

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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

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Result 16

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.

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