Trustworthy processing of healthcare big data in hybrid clouds

Managing large, heterogeneous, and rapidly increasing volumes of data, and extracting value out of such data, has long been a challenge. In the past, this was partially mitigated by fast processing technologies that exploited Moore's law. However, with a fundamental shift toward big data applications, data volumes are growing faster than they can be analyzed, regardless of increased CPU speeds or other performance improvements. Efforts thus need to focus on the development of security and privacy techniques that can deal with changing volume, velocity, and variety of heterogeneous dataflow, be ported to diverse big data programming frameworks, deal with variable computational complexity due to heterogeneous VM, storage, and network configurations across multiple clouds, and be seamlessly implemented in multicloud orchestration APIs such as jclouds.