Anonymization of multiple and personalized sensitive attributes

Jerry Chun Wei Lin; Qiankun Liu; Philippe Fournier-Viger; Youcef Djenouri; Ji Zhang

doi:10.1007/978-3-319-98539-8_16

Anonymization of multiple and personalized sensitive attributes

Jerry Chun Wei Lin^*
, Qiankun Liu
, Philippe Fournier-Viger
, Youcef Djenouri
, Ji Zhang

^*Kontaktforfatter

Institut for Matematik og Datalogi

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

Abstract

In the past, many algorithms have presented to hide the sensitive information but most of them identify the sensitive information as the same for all users/transactions, which is not a situation happened in realistic applications. In this paper, we present the (k, p)-anonymity framework to hide not only the multiple sensitive information but also the personal sensitive ones. Extensive experiments indicated that the proposed algorithm outperforms the-state-of-the-art algorithms in terms of information loss and runtime.

Originalsprog	Engelsk
Titel	Big Data Analytics and Knowledge Discovery
Redaktører	Carlos Ordonez, Ladjel Bellatreche
Forlag	Springer
Publikationsdato	aug. 2018
Sider	204-215
ISBN (Trykt)	978-3-319-98538-1
ISBN (Elektronisk)	978-3-319-98539-8
DOI	https://doi.org/10.1007/978-3-319-98539-8_16
Status	Udgivet - aug. 2018
Begivenhed	20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018 - Regensburg, Tyskland Varighed: 3. sep. 2018 → 6. sep. 2018

Konference

Konference	20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018
Land/Område	Tyskland
By	Regensburg
Periode	03/09/2018 → 06/09/2018

Navn	Lecture Notes in Computer Science
Vol/bind	11031 LNCS
ISSN	0302-9743

Finansiering

Acknowledgment. This research was partially supported by the Shenzhen Technical Project under JCYJ20170307151733005 and KQJSCX20170726103424709.

Dokumenter og links

10.1007/978-3-319-98539-8_16

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

@inproceedings{b799a6abaade4d208969e6cbf727c719,

title = "Anonymization of multiple and personalized sensitive attributes",

abstract = "In the past, many algorithms have presented to hide the sensitive information but most of them identify the sensitive information as the same for all users/transactions, which is not a situation happened in realistic applications. In this paper, we present the (k, p)-anonymity framework to hide not only the multiple sensitive information but also the personal sensitive ones. Extensive experiments indicated that the proposed algorithm outperforms the-state-of-the-art algorithms in terms of information loss and runtime.",

keywords = "Anonymization, Cluster, Hierarchical attributes, Multiple sensitive information",

author = "Lin, \{Jerry Chun Wei\} and Qiankun Liu and Philippe Fournier-Viger and Youcef Djenouri and Ji Zhang",

year = "2018",

month = aug,

doi = "10.1007/978-3-319-98539-8\_16",

language = "English",

isbn = "978-3-319-98538-1",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "204--215",

editor = "Carlos Ordonez and Ladjel Bellatreche",

booktitle = "Big Data Analytics and Knowledge Discovery",

address = "Germany",

note = "20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018 ; Conference date: 03-09-2018 Through 06-09-2018",

}

Lin, JCW, Liu, Q, Fournier-Viger, P, Djenouri, Y & Zhang, J 2018, Anonymization of multiple and personalized sensitive attributes. i C Ordonez & L Bellatreche (red), Big Data Analytics and Knowledge Discovery. Springer, Lecture Notes in Computer Science, bind 11031 LNCS, s. 204-215, 20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018, Regensburg, Tyskland, 03/09/2018. https://doi.org/10.1007/978-3-319-98539-8_16

Anonymization of multiple and personalized sensitive attributes. / Lin, Jerry Chun Wei; Liu, Qiankun; Fournier-Viger, Philippe et al.
Big Data Analytics and Knowledge Discovery. red. / Carlos Ordonez; Ladjel Bellatreche. Springer, 2018. s. 204-215 (Lecture Notes in Computer Science, Bind 11031 LNCS).

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - Anonymization of multiple and personalized sensitive attributes

AU - Lin, Jerry Chun Wei

AU - Liu, Qiankun

AU - Fournier-Viger, Philippe

AU - Djenouri, Youcef

AU - Zhang, Ji

PY - 2018/8

Y1 - 2018/8

N2 - In the past, many algorithms have presented to hide the sensitive information but most of them identify the sensitive information as the same for all users/transactions, which is not a situation happened in realistic applications. In this paper, we present the (k, p)-anonymity framework to hide not only the multiple sensitive information but also the personal sensitive ones. Extensive experiments indicated that the proposed algorithm outperforms the-state-of-the-art algorithms in terms of information loss and runtime.

AB - In the past, many algorithms have presented to hide the sensitive information but most of them identify the sensitive information as the same for all users/transactions, which is not a situation happened in realistic applications. In this paper, we present the (k, p)-anonymity framework to hide not only the multiple sensitive information but also the personal sensitive ones. Extensive experiments indicated that the proposed algorithm outperforms the-state-of-the-art algorithms in terms of information loss and runtime.

KW - Anonymization

KW - Cluster

KW - Hierarchical attributes

KW - Multiple sensitive information

U2 - 10.1007/978-3-319-98539-8_16

DO - 10.1007/978-3-319-98539-8_16

M3 - Article in proceedings

AN - SCOPUS:85052855690

SN - 978-3-319-98538-1

T3 - Lecture Notes in Computer Science

SP - 204

EP - 215

BT - Big Data Analytics and Knowledge Discovery

A2 - Ordonez, Carlos

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PB - Springer

T2 - 20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018

Y2 - 3 September 2018 through 6 September 2018

ER -

Anonymization of multiple and personalized sensitive attributes

Abstract

Konference

Finansiering

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater