To conduct data mining, we often need to collect data from various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge. The goal of this paper is to provide solutions for privacy-preserving k-nearest neighbor classification which is one of data mining tasks. Our goal is to obtain accurate data mining results without disclosing private data. We propose a formal definition of privacy and show that our solutions preserve data privacy.