Review on a Privacy-Preserving and Efficient kNearest Neighbor Model Based on k- Dimension Tree for Outsource Data
Pratiksha Bhimte, Neha V. Mogre · International Journal of Innovations in Engineering and Science · 2021
Cloud computing technology has attracted the attention of researchers and organizations because of its computing power, efficiency and durability.Cloud computing technology is used to analyze exported data into a new data usage model.However, because of the major security risks arising from computer computing, many organizations now encrypt data before extracting data.So, in recent years, many functions in the k-Nearest Neighbor (indicated by k-NN) encrypted data algorithm have emerged.However, two major problems in the current study may be that the system is not secure enough or is not working properly.In this paper, based on existing issues, we are developing a non-KNN privacy protection plan and an integration plan.Our proposed scheme uses two existing encryption schemes: Order Keep Encryption and Parlier cryptosystem, encryption of encrypted encrypted data, data access patterns, and query recording, and use dimensional tree encryption (defined by kd-tree enhancement).traditional KNN algorithm.Our proposed system aims to achieve the effectiveness of queries while ensuring data security.The comprehensive test results prove that the system is very close to the system using written data and the existing system of encrypted data queries inconsistent with classification accuracy.ours is higher than the k-NN query scheme already in effect.