An Automated Social Graph De-anonymization Technique
Event details
| Date | 04.06.2014 |
| Hour | 09:15 |
| Speaker | George DANEZIS, University College London (UCL) |
| Location | |
| Category | Conferences - Seminars |
We discuss a generic and automated approach to re-identifying nodes in anonymized social networks, that allows the fast security evaluation of novel anonymization techniques. It uses machine learning (decision forests) to matching pairs of nodes in disparate anonymized sub-graphs. The technique uncovers artefacts and invariants of any black-box anonymization scheme from a small set of examples. Despite a high degree of automation, classification succeeds with significant true positive rates even when small false positive rates are sought. Our evaluation uses publicly available real world datasets to study the performance of the techniques against real-world anonymizations strategies, namely the schemes used to protect datasets of The Data for Development (D4D) Challenge. We show the technique is effective even given few training examples, or training examples across different social networks.
Links
Practical information
- General public
- Free
Organizer
- Jean-Pierre Hubaux
Contact
- Sylvie Thomet