Data Privacy and Anonymization
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In the world of Big Data, there has been a lot of the research into creating efficient algorithms that can help us gain statistical insight from the large databases that record much of our life. However, as our digital footprint becomes larger, many databases that were originally considered anonymous can now be re-identified. How do we make sure that doesn't happen?
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Data Privacy and Anonymization
- 1. Big Data and Attacks on Privacy: How to Properly Anonymize Social Networks and Databases (and Keep Them That Way) AC 298r Final Presentation Ryan Lee and Jeffrey Wang
- 2. Obligatory Social Network Stats http://www.mediabistro.com/alltwitter/files/2013/11/growth-of-social-media-2013.jpg
- 3. Uses of Social Data: Research Bollen et al. (2011). CS109 Harvard Univ. Fall 2013 Christakis & Fowler (2010). Christakis & Fowler (2007).
- 4. Uses of Social Data: Marketing Facebook.com Bio-Rad
- 5. Chang, R., Lee, A., Ghoniem, M., Kosara, R., Ribarsky, W., Yang, J., ... & Sudjianto, A. (2008). Scalable and interactive visual analysis of financial wire transactions for fraud detection. Information visualization, 7(1), 63-76. Uses of Social Data: Government
- 7. Naive Approach: Anonymization Name Favorite Pizza Favorite Course Ryan Lee Supreme AC298r Jeffrey Wang Pepperoni AC298r Daniel Weinstock Anchovies AC298r
- 8. Naive Approach: Anonymization Name Favorite Pizza Favorite Course Ryan Lee Supreme AC298r Jeffrey Wang Pepperoni AC298r Daniel Weinstock Anchovies AC298r
- 10. Concern: Digital Footprint NSA Data Warehouse
- 11. Deanonymization is Possible Sweeny, Fuzziness and Knowledge-based Systems, 2002
- 12. Netflix Prize 2
- 13. Netflix De-anon: How they did it ● 500,000 record dataset was super-sparse Netflix “Anonymized” Data Public Data (IMDb, twitter, blogs, etc.) Match if: time < threshold movie rating < threshold Names
- 14. Surnames in Genomic Sequences TACATA is a real last name...
- 15. “Anonymized” Cell Phone Data de Montjoye, Y. A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013). Unique in the Crowd: The privacy bounds of human mobility. Scientific reports, 3.
- 17. K-Anonymity Sweeny, Fuzziness and Knowledge-based Systems, 2002
- 18. A Tough Problem DOB, Gender, and ZIP Code is enough to uniquely identify 87% of US Citizens Sweeny, Fuzziness and Knowledge-based Systems, 2002
- 19. Solution? First Last Age Race Harry Stone 34 African American John Reyser 36 Caucasian Beatrice Stone 34 African American John Delgado 22 Hispanic Sweeny, Fuzziness and Knowledge-based Systems, 2002
- 20. Solution: Suppression and Generalization First Last Age Race Harry Stone 34 African American John Reyser 36 Caucasian Beatrice Stone 34 African American John Delgado 22 Hispanic k=2: Polynomial Solution! (Simplex Matching) k>=3: NP-Hard (Graph Decomposition) Sweeny, Fuzziness and Knowledge-based Systems, 2002
- 21. ● Users are ε times less likely to be identified if they chose not to participate in the database Differential Privacy Dwork, ICALP, 2002
- 22. Anonymity in Social Networks Peter S. Bearman, James Moody, and Katherine Stovel, Chains of affection: The structure of adolescent romantic and sexual networks, American Journal of Sociology 110, 44-91 (2004). http://www-personal.umich.edu/~mejn/networks/addhealth.gif High School Dating Network
- 23. Information-rich Network Structure Backstrom, L., & Kleinberg, J. (2013). Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook. arXiv preprint arXiv:1310.6753.
- 24. Attacks on Social Networks ● Passive: Find yourselves ● Active: structural steganography http://www.cse.psu.edu/~asmith/courses/privacy598d/www/lec-notes/Attacking%20Social%20Network%20FINAL.pdf No isomorphic No automorphism
- 25. Obfuscating Social Networks Zhou and Pei, KAIS, 2011
- 26. Part 1: Construct Min-DFS Tree for Neighborhood Zhou and Pei, KAIS, 2011
- 27. 2 Useful Properties 1. Social Networks follow a Power-Law Distribution 2. Social Networks typically have a small diameter (6 degrees of separation)
- 28. Step 2: Anonymize Similar Vertices Zhou and Pei, KAIS, 2011
- 29. Step 3: ??? => Step 4: Profit! Zhou and Pei, KAIS, 2011
- 30. thanks bye