To be or not to be (anonymous)? Anonymyity in the age of big and open data.
summary
Panel contrasts the Article 29 30‑page opinion and practical toolkits: legal test of singling out and linkability plus an inference criterion; famous failures (AOL, Netflix, NYC taxi) show simple pseudonymization fails; no single technique removes all risk though differential privacy handles most; operational advice: specify intended use, select key attributes, combine methods, document parameters, run re‑identification risk analyses and penetration tests, consider functional/contextual anonymization and permutation+noise constructions, and treat anonymization as an ongoing process.
AI-written summary · about
chapters (8)
- 0:00Session introduction
- 3:12Anonymization techniques
- 15:16Audience Q&A
- 22:36Decision-making framework
- 33:23Framework Q&A
- 34:35Privacy and utility
- 44:04Ethical and legal views
- 1:01:45Final discussion and wrap-up
related talks
32mThe Dangerous Data Anonymization - Keynote
29mPersonal Data Pseudonymization Versus Anonymization In The Age Of AI & Big Data
15mHow We Can Save Anonymization
33mPractical Data Privacy
15mDe-identified, Anonymized, or Aggregated? A Look around the Globe
50mData Privacy Concerns - A Risk Based Approach to De-Identification
01h 13mThe risk-based approach to privacy: How far should we go?
01h 19mThe Synthetic Data Spectrum: Where Does Anonymisation Start and Privacy-preserving end?