1. Privacy Wizards for Social Networking Sites Reporter : 鄭志欣 Advisor: Hsing-Kuo Pao Date : 2011/01/17 1

2. 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

3. Outline  Introduction  Wizard Overview  Active Learning Wizard  Evaluation  Conclusion 3

4. 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

5. 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

6. Challenges  Low Effort, High Accuracy  Graceful Degradation  Visible Data  Incrementality 6

7. Idea 7 Idea: With limited information, build a model to predict user’s preferences, auto-configure settings

8. Wizard Overview 8

9. Active Learning Wizard  Classifier  Each friend as a feature vector  Question  How to extract features from friends?  How to solicit user input? 9

10. Extracting Features 10 AgeSexG0G0 G1G1 G2G2 G 20 G 21 G 22 G3G3 Obama Fan Pref. Label (DOB) (Alice)25F01000001allow (Bob)18M00110000deny (Carol)30F10000000? G0G0 G1G1 G2G2 G3G3 G 20 G 21 G 22

11. 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. 12 Figure 5: Screenshot of user study application, general questions Figure 6: Screenshot of user study application,detailed questions.

13. 13

14. Evaluation  Gathered raw preference data from 45 real Facebook users.  How effective is the active learning wizard, compared to alternative tools? 14

15. 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

16. Result 16

17. Tradeoff 17

18. 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