Adaptive Data Protection methodology Using K- Nearest Neighbour Query Services over the Cloud Environment

Abstract—For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed undervarious scenarios. With the recent popularity of cloud computing, 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 (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by thecloud; otherwise, there would be no point to outsource the dataat the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbour (kNN) query problem over encrypted database outsourced to a cloud:a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first presenta basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure Knn protocol that protects the confidentiality of the data, user’s input query, and data access patterns. Also, we empirically analyse the efficiency of our protocols through various experiments. The seresults indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query. I