The High School Profiling Attack: How Privacy Laws Can Increase Minors’ Risk

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 Strangersin 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. Children’s Online Privacy Protection Act 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 candidate students core users

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

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 # 1 Component # 2

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

27. CDF for School Database