  Even though cloud computing provides many intrinsic benefits, privacy
concerns related to the lack of control over the storage and management of the
outsourced data still prevent many customers from migrating to the cloud.
Several privacy-protection mechanisms based on a prior encryption of the data
to be outsourced have been proposed. Data encryption offers robust security,
but at the cost of hampering the efficiency of the service and limiting the
functionalities that can be applied over the (encrypted) data stored on cloud
premises. Because both efficiency and functionality are crucial advantages of
cloud computing, in this paper we aim at retaining them by proposing a
privacy-protection mechanism that relies on splitting (clear) data, and on the
distributed storage offered by the increasingly popular notion of multi-clouds.
We propose a semantically-grounded data splitting mechanism that is able to
automatically detect pieces of data that may cause privacy risks and split them
on local premises, so that each chunk does not incur in those risks; then,
chunks of clear data are independently stored into the separate locations of a
multi-cloud, so that external entities cannot have access to the whole
confidential data. Because partial data are stored in clear on cloud premises,
outsourced functionalities are seamlessly and efficiently supported by just
broadcasting queries to the different cloud locations. To enforce a robust
privacy notion, our proposal relies on a privacy model that offers a priori
privacy guarantees; to ensure its feasibility, we have designed heuristic
algorithms that minimize the number of cloud storage locations we need; to show
its potential and generality, we have applied it to the least structured and
most challenging data type: plain textual documents.
