Even though cloud computing provides many intrinsic benefits, privacyconcerns related to the lack of control over the storage and management of theoutsourced data still prevent many customers from migrating to the cloud.Several privacy-protection mechanisms based on a prior encryption of the datato be outsourced have been proposed. Data encryption offers robust security,but at the cost of hampering the efficiency of the service and limiting thefunctionalities that can be applied over the (encrypted) data stored on cloudpremises. Because both efficiency and functionality are crucial advantages ofcloud computing, in this paper we aim at retaining them by proposing aprivacy-protection mechanism that relies on splitting (clear) data, and on thedistributed storage offered by the increasingly popular notion of multi-clouds.We propose a semantically-grounded data splitting mechanism that is able toautomatically detect pieces of data that may cause privacy risks and split themon 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 amulti-cloud, so that external entities cannot have access to the wholeconfidential data. Because partial data are stored in clear on cloud premises,outsourced functionalities are seamlessly and efficiently supported by justbroadcasting queries to the different cloud locations. To enforce a robustprivacy notion, our proposal relies on a privacy model that offers a prioriprivacy guarantees; to ensure its feasibility, we have designed heuristicalgorithms that minimize the number of cloud storage locations we need; to showits potential and generality, we have applied it to the least structured andmost challenging data type: plain textual documents.
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