Privacy-Utility Feature Selection as a tool in Private Data Classification
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This paper presents a novel framework for privacy aware collaborative information sharing for data classification. Two data holders participating in this information sharing system, for global benefits are interested to model a classifier on whole dataset, if a certain amount of privacy is guaranteed. To address this issue, we propose a privacy mechanism approach based on privacy-utility feature selection, which by eliminating the most irrelevant set of features in terms of accuracy and privacy, guarantees the privacy requirements of data providers, whilst the data remain practically useful for classification. Due to the fact that the proposed trade-off metric is required to be exploited on whole dataset, secure weighted average protocol is utilized to protect information leakage in each site.
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This work was partially supported by the H2020 EU funded project NeCS [GA #675320] and by the H2020 EU funded project C3ISP [GA #700294].
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