Title :
Building k-nearest neighbor classifiers on vertically partitioned private data
Author :
Zhan, Justin ; Chang, LiWu ; Matwin, Stan
Author_Institution :
Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada
Abstract :
This paper considers how to conduct k-nearest neighbor classification in the following scenario: multiple parties, each having a private data set, want to collaboratively build a k-nearest neighbor classifier without disclosing their private data to each other or any other parties. Specifically, the data are vertically partitioned in that all parties have data about all the instances involved, but each party has its own view of the instances - each party works with its own attribute set. Because of privacy constraints, developing a secure framework to achieve such a computation is both challenging and desirable. In this paper, we develop a secure protocol for multiple parties to conduct the desired computation. All the parties participate in the encryption and in the computation involved in learning the k-nearest neighbor classifiers.
Keywords :
cryptography; data mining; data privacy; pattern classification; protocols; encryption; k-nearest neighbor classifier; privacy constraint; private data set; secure protocol; vertically partitioned private data; Cryptography; Data engineering; Data mining; Data privacy; Information technology; Laboratories; Nearest neighbor searches; Partitioning algorithms; Perturbation methods; Protocols;
Conference_Titel :
Granular Computing, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9017-2
DOI :
10.1109/GRC.2005.1547383