Efficient and Secure kNN Classification over Encrypted Data Using Vector Homomorphic Encryption

Abstract

The k-nearest neighbor (kNN) classification has been widely adopted in data mining applications. In the age of big data, kNN classification process has to be outsourced to the cloud. However, as data may contain sensitive information, outsourcing data services directly to public clouds inevitably raises privacy concerns. To ensure the privacy of data, it is a well- known method to encrypt them prior to uploading to the cloud, which also brings great challenges to effective kNN classification. Homomorphic encryption (HE) allows operations on encrypted data, which provides a viable solution to kNN classification over encrypted data. However, existing works using HE to enable secure kNN classification all encrypt data attribute-wise that are limited by classification efficiency. In this paper, we designed an efficient and secure kNN classification protocol over encrypted data using vector HE, namely ESkNNC, which could encrypt data record-wise. Security analysis shows that ESkNNC achieves function secrecy, besides confidentiality of data, confidentiality of query record, and hiding data access patterns. Compared with kNN classification techniques over plaintexts, ESkNNC achieves the same 98% accuracy with the precision of 2 digits. Furthermore, we propose a batching method of test data that significantly saves communication cost up to 90%.