1. The High School Profiling Attack: How Privacy Laws Can Increase Minors’ Risk Ratan Dey, Yuan Ding, Keith W. Ross Dept. of Computer Science and Engineering

2. Third-Party Profiling of Children Question: Is it possible to automatically build detailed profiles of most of the teenagers (ages 12-17) in a target high school? Profiles might include: Full name, gender, birth year, current school name, school year Home street address, photo of home SkypeID, email address Names and profiles of family members; names and profiles of school friends Interests, wall postings, hundreds of photos

3. The Danger Data brokers: sell profiles to advertisers, spammers, malware distributors, employment agencies, college admission offices. teen market surpasses $200B in US Pedophiles: many already luring victims with Facebook Spear-phishing attacks: Large-scale, automated and highly personalized

4. Natural Approach: Begin w/ Facebook Find a child on FB, download his information. Visit his friends’ pages. Repeat with friends. Then try to enhance profiles with other sources.

5. What a stranger sees about a minor:

6. What a stranger sees about an adult

7. Default and Worst-Case Information Available to Strangers in Facebook

8. Challenge For a given high school, how do we find the students in Facebook and build profiles??? – Minors are not searchable by school in FB – Only name, profile photo, cover photo album, and gender available for minor.

9. Attack Ingredients COPPA, a law designed to protect the privacy of children, indirectly facilitates the attack. “Reverse Friend Lookup,” an attacker can infer a user’s friends even if the user’s friend list is private. High-school students tend to have a relatively large number of friends from the same high school in the same graduating class year.

10. C hildren’s O nline P rivacy P rotection A ct Some children lie about their ages

11. High-School Profiling Attack Pick target HS Search FB by HS – Mostly get adults (alumni) – But get some lying minors w/ future grad year: “core users” Collect all friends of core users: “candidates” Identify candidates with many friends in core set

12. Identify candidates w/ many core friends core users candidate students

13. Lying minors in 10 th grade in Springfield HS Harry likely: lives in Springfield goes to Springfield High 10 th grade 16 years old friends with Lisa, Etienne Honest minor: name and pic Honest minors are vulnerable

14. Data sets – One private & two public high schools

15. Estimating the crawling efforts

16. High-School #1 362 students; found FB pages for 325 Attack:18 core users; 6,282 candidates Top 300 has 75% w/ 22% false negatives

17. High-School #2,3

18. Profile for honest minor: Full name, gender, profile picture City, school name, school year, birth year Friends in same school; their profiles Home street address, photo of home Names of parents SkypeID Facebook pages of parents ……

19. What if no COPPA ?

20. Counter-measure: remove Harry from others’ friend lists

21. Take away Component of COPPA law actually facilitates privacy leakages to third parties. OSNs can take additional measures to significantly protect children’s privacy. – Remove minors from public friend lists – Detect lying minors

22. Some Current/Future Research Defenses – Government polices, OSN measures – Quantify privacy leakage City attack – Attempt to find and profile all middle-school and high-school children – Active attack: “friend” minors, get more info Information from photos – Big data approach

23. IMDB Database

24. Poly Students

25. Component graphs for students Component # 1Component # 2

26. Obtaining relative height estimates 1.Use openCV for face detection 2.Use midpoints of boxes to determine height differences in pixels = p ij 3.Determine average box size in pixels = b 4.Determine height differences wrt box height 5. e.g., S = 15 cm

27. CDF for School Database