Towards Dynamic End-to-End Privacy Preserving Data Classification

Rania Talbi; Sara Bouchenak; Lydia Y. Chen

doi:10.1109/DSN-W.2018.00036

Towards Dynamic End-to-End Privacy Preserving Data Classification

Rania Talbi, Sara Bouchenak, Lydia Y. Chen

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 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 language	English
Title of host publication	Proceedings - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	73-74
Number of pages	2
ISBN (Electronic)	9781538655955
DOIs	https://doi.org/10.1109/DSN-W.2018.00036
Publication status	Published - 19 Jul 2018
Externally published	Yes
Event	48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018 - Luxembourg City, Luxembourg Duration: 25 Jun 2018 → 28 Jun 2018

Conference

Conference	48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018
Country/Territory	Luxembourg
City	Luxembourg City
Period	25/06/18 → 28/06/18

Keywords

decision trees
homomorphic encryption
incremental learning
privacy preserving data classification

Access to Document

10.1109/DSN-W.2018.00036

Cite this

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title = "Towards Dynamic End-to-End Privacy Preserving Data Classification",

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.",

keywords = "decision trees, homomorphic encryption, incremental learning, privacy preserving data classification",

author = "Rania Talbi and Sara Bouchenak and Chen, {Lydia Y.}",

year = "2018",

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doi = "10.1109/DSN-W.2018.00036",

language = "English",

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booktitle = "Proceedings - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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note = "48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018 ; Conference date: 25-06-2018 Through 28-06-2018",

}

Talbi, R, Bouchenak, S & Chen, LY 2018, Towards Dynamic End-to-End Privacy Preserving Data Classification. in Proceedings - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018., 8416221, Institute of Electrical and Electronics Engineers (IEEE), pp. 73-74, 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018, Luxembourg City, Luxembourg, 25/06/18. https://doi.org/10.1109/DSN-W.2018.00036

Towards Dynamic End-to-End Privacy Preserving Data Classification. / Talbi, Rania; Bouchenak, Sara; Chen, Lydia Y.
Proceedings - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018. Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 73-74 8416221.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Towards Dynamic End-to-End Privacy Preserving Data Classification

AU - Talbi, Rania

AU - Bouchenak, Sara

AU - Chen, Lydia Y.

PY - 2018/7/19

Y1 - 2018/7/19

N2 - 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.

AB - 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.

KW - decision trees

KW - homomorphic encryption

KW - incremental learning

KW - privacy preserving data classification

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DO - 10.1109/DSN-W.2018.00036

M3 - Conference contribution

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BT - Proceedings - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, DSN-W 2018

PB - Institute of Electrical and Electronics Engineers (IEEE)

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Y2 - 25 June 2018 through 28 June 2018

ER -

Towards Dynamic End-to-End Privacy Preserving Data Classification

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Keywords

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