Towards Privacy Preserving Publishing of Set-Valued Data on Hybrid Cloud - 2018


Storage as a service has become an necessary paradigm in cloud computing for its great flexibility and economic savings. But, the development is hampered by information privacy considerations: data homeowners now not physically possess the storage of their information. In this work, we tend to study the issue of privacy-preserving set-valued data publishing. Existing data privacy-preserving techniques (such as encryption, suppression, generalization) are not applicable in several real scenes, since they would incur giant overhead for data question or high information loss. Motivated by this observation, we have a tendency to gift a set of recent techniques that build privacy-aware set-valued information publishing possible on hybrid cloud. On knowledge publishing phase, we have a tendency to propose a information partition technique, named extended quasi-identifierpartitioning (EQI-partitioning), that disassociates record terms that participate in identifying combos. This method the cloud server cannot associate with high probability a record with rare term combinations. We prove the privacy guarantee of our mechanism. On knowledge querying part, we tend to adopt interactive differential privacy strategy to resist privacy breaches from statistical queries. We have a tendency to finally evaluate its performance using real-life information sets on our cloud check-bed. Our intensive experiments demonstrate the validity and practicality of the proposed scheme.

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