A Study of Privacy Preserving Methods for kNN Computation on Encrypted Data

Abstract With the emergence of cloud computing as platform for deploying, processing and carrying out large- scale operations, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data need to be encrypted before outsourcing it to the cloud. It is a challenging job to process database queries over encrypted data without the cloud disturbing the confidentiality of the data and user’s query. Moreover, for specific applications such as classification of user query record, the confidentiality of the data access patterns also needs to be maintained. In this paper, we provide an overview on how the issue of maintaining privacy of a database query is handled when it is triggered for an encrypted database. Also, we focus on knearest neighbor (kNN) computation problem on an encrypted database. We also review the different techniques and supporting scheme that are incorporated for dealing with this issue.