Building k-nearest neighbor classifiers on vertically partitioned private data

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.