Download as PDF, PPTX
Uploaded byJeffrey Wang
Data Privacy and Anonymization
The document discusses the challenges of maintaining privacy in social networks and databases, highlighting issues with naive anonymization techniques. It cites various studies on the uses of social data in research, marketing, and government, and explores concepts like k-anonymity and differential privacy as potential solutions. Additionally, it emphasizes the ease of deanonymization through unique identifiers and the need for more robust methods to protect user privacy.
More Related Content
Similar to Data Privacy and Anonymization
Data Privacy and Anonymization
- 1. Big Data andAttacks 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 NetworkStats http://www.mediabistro.com/alltwitter/files/2013/11/growth-of-social-media-2013.jpg
- 3. Uses of SocialData: Research Bollen et al. (2011). CS109 Harvard Univ. Fall 2013 Christakis & Fowler (2010). Christakis & Fowler (2007).
- 4. Uses of SocialData: 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
- 6.
- 7. Naive Approach: Anonymization NameFavorite Pizza Favorite Course Ryan Lee Supreme AC298r Jeffrey Wang Pepperoni AC298r Daniel Weinstock Anchovies AC298r
- 8. Naive Approach: Anonymization NameFavorite Pizza Favorite Course Ryan Lee Supreme AC298r Jeffrey Wang Pepperoni AC298r Daniel Weinstock Anchovies AC298r
- 9.
- 10. Concern: Digital Footprint NSAData Warehouse
- 11. Deanonymization is Possible Sweeny,Fuzziness and Knowledge-based Systems, 2002
- 12.
- 13. Netflix De-anon: Howthey 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 GenomicSequences TACATA is a real last name...
- 15. “Anonymized” Cell PhoneData 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.
- 16.
- 17. K-Anonymity Sweeny, Fuzziness andKnowledge-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 AgeRace 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 FirstLast 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 areε times less likely to be identified if they chose not to participate in the database Differential Privacy Dwork, ICALP, 2002
- 22. Anonymity in SocialNetworks 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 SocialNetworks ● 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 Zhouand Pei, KAIS, 2011
- 26. Part 1: ConstructMin-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: AnonymizeSimilar Vertices Zhou and Pei, KAIS, 2011
- 29. Step 3: ???=> Step 4: Profit! Zhou and Pei, KAIS, 2011
- 30.