Name
Abstract | ||
|---|---|---|
Privacy and security concerns can prevent sharing of data, derailing data-mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. We introduce a generalized privacy-preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with a proof of security, we discuss what would be necessary to make the protocols completely secure. We also provide experimental results, giving a first demonstration of the practical complexity of secure multiparty computation-based data mining. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1409620.1409624 | DBSec |
Keywords | DocType | Volume |
secure multiparty computation,privacy,knowledge discovery,data mining,decision tree | Journal | 2 |
Issue | ISSN | ISBN |
3 | 0302-9743 | 3-540-28138-X |
Citations | PageRank | References |
41 | 1.44 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaideep Vaidya | 1 | 2778 | 171.18 |
| Chris Clifton | 2 | 3327 | 544.44 |
| Murat Kantarcioglu | 3 | 2470 | 168.03 |
| A. Scott Patterson | 4 | 41 | 1.44 |