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).  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: 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 AgeSexG0G0 G1G1 G2G2 G 20 G 21 G 22 G3G3 Obama Fan Pref. Label (DOB) (Alice)25F allow (Bob)18M deny (Carol)30F ? G0G0 G1G1 G2G2 G3G3 G 20 G 21 G 22

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.

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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? 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