Towards Dynamic End-to-End Privacy Preserving Data Classification

Rania Talbi, Sara Bouchenak, Lydia Y. Chen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages73-74
Number of pages2
ISBN (Electronic)9781538655955
DOIs
Publication statusPublished - 19 Jul 2018
Externally publishedYes
Event48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018 - Luxembourg City, Luxembourg
Duration: 25 Jun 201828 Jun 2018

Conference

Conference48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018
CountryLuxembourg
CityLuxembourg City
Period25/06/1828/06/18

Keywords

  • decision trees
  • homomorphic encryption
  • incremental learning
  • privacy preserving data classification

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