Efficient privacy-preserving k-nearest neighbors in cloud computing

In the face of the increasing volume of data stored today, various enterprise organization outsources their databases to cloud server vendors for storage and management in order on database management costs. As enterprise organization encrypts their data for security reasons when uploading, this can create certain problems for the usability of the data; it is necessary to implement a kNN solution that supports secure queries over ciphertext. A number of approaches on its privacy protection have emerged, but there are still issues such as efficiency and security. To address these issues, an efficient and secure k-neighborhood query scheme SEkNN (Secure and efficient kNN) over cloud computing was proposed using garbed circuits combining secret sharing and Yao protocol to construct Euclidean computation as well as a secure ordering algorithm that works completely secure and more efficient. Security analysis was also performed to prove that the scheme was secure under the semi-honest model. The final experiment was compared with a lightweight kNN scheme based on edge computing (EBkNN) and verified that the scheme better meets the needs of practical application.