  An increasing number of businesses are replacing their data storage and
computation infrastructure with cloud services. Likewise, there is an increased
emphasis on performing analytics based on multiple datasets obtained from
different data sources. While ensuring security of data and computation
outsourced to a third party cloud is in itself challenging, supporting
analytics using data distributed across multiple, independent clouds is even
further from trivial. In this paper we present CloudMine, a cloud-based service
which allows multiple data owners to perform privacy-preserved computation over
the joint data using their clouds as delegates. CloudMine protects data privacy
with respect to semi-honest data owners and semi-honest clouds. It furthermore
ensures the privacy of the computation outputs from the curious clouds. It
allows data owners to reliably detect if their cloud delegates have been lazy
when carrying out the delegated computation. CloudMine can run as a centralized
service on a single cloud, or as a distributed service over multiple,
independent clouds. CloudMine supports a set of basic computations that can be
used to construct a variety of highly complex, distributed privacy-preserving
data analytics. We demonstrate how a simple instance of CloudMine (secure sum
service) is used to implement three classical data mining tasks
(classification, association rule mining and clustering) in a cloud
environment. We experiment with a prototype of the service, the results of
which suggest its practicality for supporting privacy-preserving data analytics
as a (multi) cloud-based service.

Cryptography and Security;Distributed, Parallel, and Cluster Computing