Name
Abstract | ||
|---|---|---|
It is important to study the risks of publishing privacy-sensitive data. Even if sensitive identities (e.g., name, social security number) were removed and advanced data perturbation techniques were applied, several de-anonymization attacks have been proposed to re-identify individuals. However, existing attacks have some limitations: 1) they are limited in de-anonymization accuracy; 2) they require prior seed knowledge and suffer from the imprecision of such seed information.
We propose a novel structure-based de-anonymization attack, which does not require the attacker to have prior information (e.g., seeds). Our attack is based on two key insights: using multi-hop neighborhood information, and optimizing the process of de-anonymization by exploiting enhanced machine learning techniques. The experimental results demonstrate that our method is robust to data perturbations and significantly outperforms the state-of-the-art de-anonymization techniques by up to 10x improvement.
|
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3139550.3139562 | workshop on privacy in the electronic society |
Field | DocType | Volume |
Social network,De-anonymization,Computer security,Computer science,Publishing,Social Security number | Conference | abs/1801.05534 |
ISBN | Citations | PageRank |
978-1-4503-5175-1 | 1 | 0.35 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei-Han Lee | 1 | 45 | 6.54 |
| Changchang Liuc | 2 | 56 | 4.65 |
| Shouling Ji | 3 | 616 | 56.91 |
| Prateek Mittal | 4 | 1134 | 70.19 |
| Ruby Lee | 5 | 2460 | 261.28 |