A Privacy-Preserving and Efficient k-Nearest Neighbor Query and Classification Scheme Based on k-Dimensional Tree for Outsourced Data

Abstract

Cloud computing technology has attracted the attention of researchers and organizations due to its computing power, computing efficiency and flexibility. Using cloud computing technology to analysis outsourced data has become a new data utilization model. However, due to the severe security risks that appear in cloud computing, most organizations now encrypt data before outsourcing data. Therefore, in recent years, many new works on the k-Nearest Neighbor (denoted by k-NN) algorithm for encrypted data has appeared. However, two main problems are existing in the current research: either the program is not secure enough or inefficient. In this paper, based on the existing problems, we design a non-interactive privacy-preserving k-NN query and classification scheme. Our proposed scheme uses two existing encryption schemes: Order Preserving Encryption and the Paillier cryptosystem, to preserve the confidentiality of encrypted outsourced data, data access patterns, and the query record, and utilizes the encrypted the k-dimensional tree (denoted by kd-tree) to optimize the traditional k-NN algorithm. Our proposed scheme strives to achieve high query efficiency while ensuring data security. Extensive experimental results prove that this scheme is very close to the scheme using plaintext data and the existing non-interactive encrypted data query scheme in terms of classification accuracy. The query runtime of our scheme is superior to the existing non-interactive k-NN query scheme.