Name
Abstract | ||
|---|---|---|
Online social networks, such as Facebook, are increasingly utilized by many people. These networks allow users to publish details about themselves and to connect to their friends. Some of the information revealed inside these networks is meant to be private. Yet it is possible to use learning algorithms on released data to predict private information. In this paper, we explore how to launch inference attacks using released social networking data to predict private information. We then devise three possible sanitization techniques that could be used in various situations. Then, we explore the effectiveness of these techniques and attempt to use methods of collective inference to discover sensitive attributes of the data set. We show that we can decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods we described. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TKDE.2012.120 | Knowledge and Data Engineering, IEEE Transactions |
Keywords | Field | DocType |
data privacy,inference mechanisms,learning (artificial intelligence),social networking (online),Facebook,collective inference,learning algorithms,private information inference attacks,private information prediction,sanitization techniques,social networking data,Social network analysis,data mining,social network privacy | Publication,Data science,Social network,Problem domain,Inference,Computer science,Support vector machine,Statistical classification,Private information retrieval | Journal |
Volume | Issue | ISSN |
25 | 8 | 1041-4347 |
Citations | PageRank | References |
46 | 1.48 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raymond Heatherly | 1 | 301 | 16.43 |
| Murat Kantarcioglu | 2 | 2470 | 168.03 |
| Bhavani M. Thuraisingham | 3 | 2587 | 282.14 |