Review on a Privacy-Preserving and Efficient kNearest Neighbor Query and Classification Scheme Based on k-Dimension Tree for Outsource Data
Pratiksha Bhimte · International Journal of Innovations in Engineering and Science · 2021
Cloud computing technology has attracted the attention of researchers and organizations due to its computing power, efficiency and flexibility.Using cloud computing technology to analyze outsourced data is 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 works on the k-Nearest Neighbor (denoted by k-NN) algorithm for encrypted data has appeared.However, two main problems in existing current research are either the program is not secure enough or inefficient.In this paper, based on the existing problems, we have designed a non-interactive privacy-preserving k-query and classification scheme.Our proposed scheme uses two existing encryption schemes: Order Preserving Encryption and the Parlier cryptosystem, to preserve the privacy 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 aim to achieve high query efficiency while ensuring data security.Extensive experimental results prove that this scheme is almost 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 higher than the existing non interactive k-NN query scheme.