This paper explores secure data processing in hybrid clouds wherein local computing capability is exploited alongside public cloud services to deliver an efficient and secure data management solution. Hybrid clouds offer numerous advantages including the ability to selectively outsource data and computations based on sensitivity/confidentiality. Data processing in hybrid clouds must address two interrelated challenges: (i) data distribution: how is data distributed across public and private machines, and (ii) distributed query processing: how are queries executed efficiently without leaking sensitive data to untrusted public machines. This paper addresses these challenges and incorporates the respective solutions into an add-on tool for a Hadoop, Spark, and Hive based cloud computing infrastructure. Our results show performance advantages in using our strategy as compared to other secure alternatives, even when the percentage of sensitive data is as high as 50%.

This paper describes SEMROD, a sensitive data aware MapReduce (MR) framework for hybrid clouds. SEMROD steers data and computation through public and private machines in such a way that no knowledge about sensitive data is leaked to public machines. For this purpose, SEMROD keeps trace of intermediate keys (generated during MR execution) that become sensitive, based on which it makes dynamic task scheduling decisions. SEMROD guarantees that adversaries viz. public machines) cannot gain any ``additional'' information about sensitive data from either the data stored on public machines or the communication between public and private machines during job execution. SEMROD extends naturally from a single MR job to multi-phase MR jobs that result, for instance, from compiling Hive queries into MR jobs. Using SEMROD, computation that may involve sensitive data can exploit public machines, thereby bringing significant performance benefits. Such computation would otherwise be restricted to only private clouds. Our experiments clearly demonstrate performance advantages to using SEMROD as compared with other secure alternatives, even when the percentage of sensitive data is as high as 50%.

Data declustering and replication can be used to reduce I/O times related with processing of data intensive queries. Declustering parallelizes the query retrieval process by distributing the data items requested by queries among several disks. Replication enables alternative disk choices for individual disk items and thus provides better query parallelism options. In general, existing replicated declustering schemes do not consider query log information and try to optimize all possible queries for a specific query type, such as range or spatial queries. In such schemes, it is assumed that two or more copies of all data items are to be generated and scheduling of these copies to disks are discussed. However, in some applications, generation of even two copies of all of the data items is not feasible, since data items tend to have very large sizes. In this work, we assume that there is a given limit on disk capacities and thus on replication amounts. We utilize existing query-log information to propose a selective replicated declustering scheme, in which we select the data items to be replicated and decide on their scheduling onto disks while respecting disk capacities. We propose and implement an iterative improvement algorithm to obtain a two-way replicated declustering and use this algorithm in a recursive framework to generate a multiway replicated declustering. Then we improve the obtained multiway replicated declustering by efficient refinement heuristics. Experiments conducted on realistic data sets show that the proposed scheme yields better performance results compared to existing replicated declustering schemes.

This paper explores an efficient and secure mechanism to partition computations across public and private machines in a hybrid cloud setting. We propose a principled framework for distributing data and processing in a hybrid cloud that meets the conflicting goals of performance, sensitive data disclosure risk and resource allocation costs. The proposed solution is implemented as an add-on tool for a Hadoop and Hive based cloud computing infrastructure. Our experiments demonstrate that the developed mechanism can lead to a major performance gain by exploiting both the hybrid cloud components without violating any pre-determined public cloud usage constraints.