Name
Abstract | ||
|---|---|---|
Spontaneous formation of peer-to-peer agent-based data mining systems seems a plausible scenario in years to come. However, the emergence of peer-to-peer environments further exacerbates privacy and security concerns that arise when performing data mining tasks. We analyze potential threats to data privacy in a peer-to-peer agent-based distributed data mining scenario, and discuss inference attacks which could compromise data privacy in a peer-to-peer distributed clustering scheme known as KDEC. |
Year | Venue | Keywords |
|---|---|---|
2004 | FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS | data privacy,data mining |
Field | DocType | Volume |
Data mining,Data stream mining,Peer-to-peer,Inference,Homogeneous,Computer science,Computer security,Compromise,Information privacy,Cluster analysis | Conference | 110 |
ISSN | Citations | PageRank |
0922-6389 | 6 | 0.52 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Josenildo Costa da Silva | 1 | 42 | 4.39 |
| Matthias Klusch | 2 | 2591 | 271.67 |
| Stefano Lodi | 3 | 231 | 21.96 |
| G. Moro | 4 | 192 | 16.25 |