Abstract. Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. In this paper, we tackle the problem of classification. We introduce a generalized privacy preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with the algorithm, we give a complete proof of security that gives a tight bound on the information revealed.
In REF , it is introduced a generalized privacy preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties.
Privacy-preserving decision trees over vertically partitioned data
In Proceedings of the IFIP WG 11.3 International Conference on Data and Applications Security
Computer Science