Name
Abstract | ||
|---|---|---|
Motivated by many existing security and privacy applications, e.g., network traffic attribution, linkage attacks, private web search, and feature-based data de-anonymization, in this paper, we study the Feature-based Data Inferability (FDI) quantification problem. First, we conduct the FDI quantification under both naive and general data models from both a feature distance perspective and a feature distribution perspective. Our quantification explicitly shows the conditions to have a desired fraction of the target users to be Top-K inferable (K is an integer parameter). Then, based on our quantification, we evaluate the user inferability in two cases: network traffic attribution in network forensics and feature-based data de-anonymization. Finally, based on the quantification and evaluation, we discuss the implications of this research for existing feature-based inference systems. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Cryptography and Security | Journal |
Volume | Citations | PageRank |
abs/1902.00714 | 0 | 0.34 |
References | Authors | |
9 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shouling Ji | 1 | 616 | 56.91 |
| Haiqin Weng | 2 | 5 | 3.72 |
| Yiming Wu | 3 | 17 | 3.04 |
| Pan Zhou | 4 | 382 | 62.71 |
| Qinming He | 5 | 371 | 41.53 |
| Raheem Beyah | 6 | 213 | 14.78 |
| Ting Wang | 7 | 664 | 65.43 |