An Improved Algorithm for Querying Encrypted Data in the Cloud

Organizations have begun outsourcing management of their data to third party cloud service providers after the introduction of Database as a Service (DAS) model. A cloud database is a database that typically runs on a cloud computing platform, such as Amazon EC2, GoGrid, Salesforce and Rackspace. But outsourcing the data raises concerns over privacy. A typical solution is to store databases in encrypted form on the remote server. Queried records are downloaded from the server and decrypted for further processing. Bucketization is one technique for executing queries over encrypted data on a DAS server. This paper is an extension to work done by other researchers [1-4]. Query Optimal Bucketization (QOB) algorithm [1-2] divides the server data into buckets subject to an optimality constraint. In an earlier paper [3], the authors proposed Binary Query Bucketization (BQB) to improve the search time for bucketized datasets and reduce the number of records that are processed by QOB. In this paper, we propose a Parallel Binary Query Bucketization (PBQB) algorithm to query records located in the DAS. It integrates parallel search [4] and BQB. Parallel search divides the search workload into chunks with each thread/processor working on a chunk. Simulation is used to assess the numerical performance of PBQB. It is shown that the proposed algorithm outperforms BQB.