Privacy in Practice with Smart Pseudonymization: Lessons from the Belgian Public Sector
summary
Three concrete systems are described with code and measurements: format-preserving pseudonymization using 32‑byte symmetric keys and a REST API to replace SSNs for test environments (keeps legacy formats); blind pseudonymization that blinds/unblinds and encrypts pseudonyms so prescription services, doctors and the key manager each see only what they must (live in Belgian e‑health); and oblivious join that joins 200k / 500k / 1M inputs to produce a 50k intersection in under two minutes with a few hundred MB exchanged, backed by pending academic proofs.
AI-written summary · about
chapters (13)
- 0:00Introduction to talk
- 0:26History of pseudonyms
- 2:12Smart pseudonymization overview
- 3:14Problem with test data
- 7:28Format preserving pseudonymization
- 12:02Pseudonymization service design
- 17:20Health blind pseudonymization
- 19:07Referral prescriptions use case
- 27:33Partial encryption in healthcare
- 34:00Oblivious join for research
- 43:35Oblivious join in practice
- 46:23Academic validation of oblivious join
- 48:56Conclusion and Q&A
related talks
33mPractical Data Privacy
29mPersonal Data Pseudonymization Versus Anonymization In The Age Of AI & Big Data
32mThe Dangerous Data Anonymization - Keynote
39mPrivacy-preserving Data Sharing
01h 17mTo be or not to be (anonymous)? Anonymyity in the age of big and open data.
10mHealth Data Anonymization and Privacy
15mHow We Can Save Anonymization
45mZero Knowledge; Meeting the Growing Demand for Security and Privacy
