STBucket: A Self-Tuning Bucket Index in DAS Paradigm

In the Database-As-a-Service (DAS) paradigm, data owners outsource their data to the third-party service provider. Since the service provider is untrusted, the data should be encrypted before outsourced. Various approaches have been proposed to query on encrypted data, among which bucket based method is effective. However, previous researches just look at the data distribution with respect to a given workload, which is ineffective in changing workload behaviors. In this paper, we propose a Self-Tuning Bucket scheme: STBucket. By gathering and analyzing query feedback, STBucket achieves adaptation to workload through online bucket splitting and merging. Experimental results show that STBucket is workload aware and performs well with reasonable overhead.