A new framework of privacy preserving data sharing

We introduce a dataset reconstruction based framework for data sharing with privacy preserving. The proposed framework uses a constraint-based inverse itemset lattice mining technique to automatically generate a sample dataset to be released for sharing. In this framework, data owners can control the potential mine-able knowledge (frequent itemsets in our context) from the released dataset. Before generating the sample dataset, the potential mine-able knowledge set is checked for two aspects: One is for the compliancy of user-specified security constraint and the trade-off principle, so that the sensitive patterns are well protected while the side-effect is tolerable. The other check is verification for consistency among itemset supports in the lattice so that it is sensible for inverse dataset reconstruction. This mechanism offers the data owner total control of the potentially discoverable knowledge from publicly accessible datasets, and at the same time the released data matches with the main features of the original dataset for sharable knowledge, thus the user’s privacy can be preserved.

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