k-nearest neighbor (kNN) classifier has wide applications in many areas such as bioinformatics, stock forecasting, Web-page classification, and Iris classification prediction. With the increasing awareness of user privacy protection, kNN classifier classification also needs to provide supports for encrypted data, so privacy-preserving kNN classifier (PP-kNN) is designed to keep the privacy of user data. Firstly, the operation of kNN classifier is analyzed, and a set of basic operations is extracted, including addition, multiplication, comparison, inner product, etc. Then, two homomorphic encryption schemes and one fully homomorphic encryption scheme are selected to encrypt the data. Security protocols are designed for each of these, which outputs are consistent with the same operation over plaintext data and proved that protocol is secure in the semi-honest model. Finally, these security protocols are designed in a modules composable way to achieve the encryption of the kNN classifier. The PP-kNN classifier is implemented and evaluated based on real data, the result show that the classifier could classify the ciphertext data with higher efficiency, and also provide privacy protection for user data.