Cloud Computing with its dynamic pay as you go model and scalability characteristics promises computing on demand with associated cost savings compared to traditional computing architectures. This is a promising computing model especially in the context of Big Data. However, renting computing capabilities from a cloud provider means the integration of external resources into the own infrastructure and this requires a great amount of trust and raises new data security and privacy challenges. With respect to these still unsolved problems, this work presents a fixed vertical partitioning and distribution approach that uses traditional relational data models and distributes the corresponding partitions vertically across different cloud providers. So, every cloud provider only gets a small, but defined (and therefore fixed) logically independent chunk of the entire data, which is useless without the other parts. However, the distribution and the subsequent join of the data suffer from great performance losses, which are unbearable in practical usage scenarios. The novelty of our approach combines the well-known vertical database partitioning technique with a distribution logic that stores the vertical partitions at different cloud computing environments. Traditionally, vertical as well as horizontal partitioning approaches are used to improve the access to database data, but these approaches use dynamic and automated partitioning algorithms and schemes based on query workloads, data volumes, network bandwidth, etc. In contrast to this, our approach uses a fixed user-defined vertical partitioning approach, where no two critical attributes of a relation should be stored in a single partition. Thus, our approach aims at improving data security and privacy especially in public Cloud Computing environments, but raises the challenging research question of how to improve the data access to such fixed user-partitioned and distributed database environments. In this paper, we outline a query rewriting approach that parallelizes queries and joins in order to improve the query performance. We implemented our fixed partitioning and distribution approach based on the TPC-W benchmark and we finally present the performance results in this work.
