  Several researchers have proposed solutions for secure data outsourcing on
the public clouds based on encryption, secret-sharing, and trusted hardware.
Existing approaches, however, exhibit many limitations including high
computational complexity, imperfect security, and information leakage. This
chapter describes an emerging trend in secure data processing that recognizes
that an entire dataset may not be sensitive, and hence, non-sensitivity of data
can be exploited to overcome some of the limitations of existing
encryption-based approaches. In particular, data and computation can be
partitioned into sensitive or non-sensitive datasets - sensitive data can
either be encrypted prior to outsourcing or stored/processed locally on trusted
servers. The non-sensitive dataset, on the other hand, can be outsourced and
processed in the cleartext. While partitioned computing can bring new
efficiencies since it does not incur (expensive) encrypted data processing
costs on non-sensitive data, it can lead to information leakage. We study
partitioned computing in two contexts - first, in the context of the hybrid
cloud where local resources are integrated with public cloud resources to form
an effective and secure storage and computational platform for enterprise data.
In the hybrid cloud, sensitive data is stored on the private cloud to prevent
leakage and a computation is partitioned between private and public clouds.
Care must be taken that the public cloud cannot infer any information about
sensitive data from inter-cloud data access during query processing. We then
consider partitioned computing in a public cloud only setting, where sensitive
data is encrypted before outsourcing. We formally define a partitioned security
criterion that any approach to partitioned computing on public clouds must
ensure in order to not introduce any new vulnerabilities to the existing secure
solution.
