Name
Abstract | ||
|---|---|---|
AbstractThe increasing popularity of social networks in various application domains has raised privacy concerns for the individuals involved. In this work, we formally present the definition of profile privacy leakage, which is a newly identified privacy leakage in social networks. However, applying existing methods straightforwardly cannot provide efficient privacy protection for the profiles meanwhile incurring a large amount of information loss. We propose k-obfuscation to protect profiles against graph property based attacks. We develop a general framework for obtaining k-obfuscation. In this framework, we propose a novel safe vertex-profile mapping mechanism, named as k-mapping. We also design a number of techniques to make the k-mapping method efficient meanwhile maintaining the data utilities. Extensive experiments on real datasets show the satisfactory performance of our methods in terms of privacy protection, efficiency, and practical utilities. Copyright © 2014 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1002/sec.871 | Periodicals |
Keywords | Field | DocType |
privacy,social network,anonymity,profile | Internet privacy,Information loss,Social network,Privacy by Design,Graph property,Computer security,Computer science,Popularity,Anonymity,Obfuscation,Privacy software | Journal |
Volume | Issue | ISSN |
7 | 9 | 1939-0114 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyu Liu | 1 | 51 | 14.10 |
| Bin Wang | 2 | 1788 | 246.68 |
| Xiaochun Yang | 3 | 440 | 52.12 |
| Meng Yu | 4 | 524 | 66.52 |
| Wanyu Zang | 5 | 193 | 21.20 |