Name
Abstract | ||
|---|---|---|
Since privacy information can be inferred via social relations, the privacy confidentiality problem becomes increasingly challenging as online social network services are more popular. Using a Bayesian network approach to model the causal relations among people in social networks, we study the impact of prior probability, influence strength, and society openness to the inference accuracy on a real online social network. Our experimental results reveal that personal attributes can be inferred with high accuracy especially when people are connected with strong relationships. Further, even in a society where most people hide their attributes, it is still possible to infer privacy information. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11760146_14 | ISI |
Keywords | Field | DocType |
social network,online social network service,society openness,privacy confidentiality problem,bayesian network approach,real online social network,inference accuracy,privacy information,inferring privacy information,social relation,high accuracy | Social relation,Data mining,Social network,Bayesian inference,Confidentiality,Computer science,Inference,Openness to experience,Bayesian network,Privacy software | Conference |
Volume | ISSN | ISBN |
3975 | 0302-9743 | 3-540-34478-0 |
Citations | PageRank | References |
60 | 4.07 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianming He | 1 | 60 | 4.74 |
| Wesley W. Chu | 2 | 2311 | 789.42 |
| Zhenyu (Victor) Liu | 3 | 60 | 4.07 |