Abstract Building Decision Tree Classifier on Private Data ∗
Abstract:
This paper studies how to build a decision tree classifier under the following scenario: a database is vertically partitioned into two pieces, with one piece owned by Alice and the other piece owned by Bob. Alice and Bob want to build a decision tree classifier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces to the other party or to any third party. We present a protocol that allows Alice and Bob to conduct such a classifier building without having to compromise their privacy. Our protocol uses an untrusted third-party server, and is built upon a useful building block, the scalar product protocol. Our solution to the scalar product protocol is more efficient than any existing solutions.
Citations
| 330 | Srikant: “Privacy-Preserving Data Mining – Agrawal, R - 2000 |
| 266 | How to Play Any Mental Game – Goldreich, Micali, et al. |
| 173 | On the Design and Quantification of Privacy Preserving Data Mining Algorithms – Agrawal, Aggarwal - 2001 |
| 85 | Secure multi-party computation (working draft). www.wisdom.weizmann.ac.il/oded/pp.html – Goldreich - 2000 |
| 45 | Multi-party computations: Past and present – Goldwasser |
| 32 | Universal service-providers for database private information retrieval – Di-Crescenzo, Ishai, et al. - 1998 |
| 29 | Secure multi-party computational geometry – Atallah, Du - 2001 |
| 23 | An overview of secure distributed computing – Franklin, Galil, et al. - 1992 |
| 18 | Commodity-based cryptography – Beaver - 1997 |
| 16 | A practical approach to solve secure multiparty computation problems – Du, Zhan - 2002 |
| 15 | A Study of Several Specific Secure Two-party Computation Problems – Du - 2001 |
| 10 | Commodity-based cryptography (extended abstract – Beaver - 1997 |
