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.
|Original language||English (US)|
|Journal||ACM Transactions on Knowledge Discovery from Data|
|State||Published - Oct 1 2008|
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Decision tree classification