M-Privacy for Collaborative Data Publishing !!!!!!

In this paperwe consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providersWe consider a new type of “insider attack” by colluding data providers who may use their own data records (a subset of the overall data) to infer the data records contributed by other data providersThe paper addresses this new threatand makes several contributionsFirstwe introduce the notion of m-privacywhich guarantees that the anonymized data satisfies a given privacy constraint against any group of up to m colluding data providersSecondwe present heuristic algorithms exploiting the DOTNET monotonicity of privacy constraints for efficiently checking m-privacy given a group of recordsThirdwe present a data provider-aware anonymization algorithm with adaptive m-privacy checking strategies to ensure high utility and m-privacy of anonymized data with efficiencyFinallywe propose secure multi-party computation protocols for collaborative data publishing with m-privacyAll protocols are extensively analyzed and their security and efficiency are formally provedExperiments on real-life datasets suggest that our approach achieves better or comparable utility and efficiency than existing and baseline algorithms while satisfying m-privacy.www.fu-vision .com
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