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- W3132999349 abstract "In the era of cloud computing and machine learning, data has become a highly valuable resource. Recent history has shown that the benefits brought forth by this data driven culture come at a cost of potential data leakage. Such breaches have a devastating impact on individuals and industry, and lead the community to seek privacy preserving solutions. A promising approach is to utilize Fully Homomorphic Encryption ((mathsf {FHE })) to enable machine learning over encrypted data, thus providing resiliency against information leakage. However, computing over encrypted data incurs a high computational overhead, thus requiring the redesign of algorithms, in an “(mathsf {FHE })-friendly” manner, to maintain their practicality." @default.
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- W3132999349 date "2021-01-01" @default.
- W3132999349 modified "2023-10-14" @default.
- W3132999349 title "Privacy-Preserving Decision Trees Training and Prediction" @default.
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- W3132999349 doi "https://doi.org/10.1007/978-3-030-67658-2_9" @default.
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