Towards Dynamic End-to-End Privacy Preserving Data Classification

Talbi, Rania; Bouchenak, Sara; Chen, Lydia Y.

Towards Dynamic End-to-End Privacy Preserving Data Classification

Conference paper (2018)

Authors

Rania Talbi INSA Lyon

Sara Bouchenak INSA Lyon

Lydia Y. Chen Zurich Lab, IBM Research

Affiliation

External organisation

Decision trees Homomorphic encryption Incremental learning Privacy preserving data classification

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Published Date

19-07-2018

Language

English

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Abstract

In this paper we present DAPPLE, a standalone End-to-End privacy preserving data classification service. It allows incremental decision tree learning over encrypted training data continuously sent by multiple data owners, without having access to the actual content of this data. In the same time, the learnt classification model is used to respond to encrypted classification queries while preserving the privacy of the query, the output corresponding to it and the model itself.