A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining

Lei Xu; Chunxiao Jiang; Yan Chen; Jian Wang; Yong Ren

doi:10.1109/MC.2016.43

A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining

Lei Xu, Chunxiao Jiang, Yan Chen, Jian Wang, Yong Ren

Research output: Contribution to specialist publication › Article

48 Citations (Scopus)

Abstract

To protect sensitive information in mined data, researchers need a way to organize a variety of ongoing work. The Rampart framework categorizes protection approaches and encourages interdisciplinary solutions to the growing variety of privacy problems associated with knowledge discovery from data.

Original language	English
Pages	54-62
Number of pages	9
Volume	49
No.	2
Specialist publication	Computer
DOIs	https://doi.org/10.1109/MC.2016.43
Publication status	Published - Feb 2016
Externally published	Yes

Keywords

big data
big data management
data mining
game theory
information privacy
information security
privacy
provenance
sensitive information

Access to Document

10.1109/MC.2016.43

Cite this

@misc{4ba51072260649518b1edea94b67e3c4,

title = "A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining",

abstract = "To protect sensitive information in mined data, researchers need a way to organize a variety of ongoing work. The Rampart framework categorizes protection approaches and encourages interdisciplinary solutions to the growing variety of privacy problems associated with knowledge discovery from data.",

keywords = "big data, big data management, data mining, game theory, information privacy, information security, privacy, provenance, sensitive information",

author = "Lei Xu and Chunxiao Jiang and Yan Chen and Jian Wang and Yong Ren",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.",

year = "2016",

month = feb,

doi = "10.1109/MC.2016.43",

language = "English",

volume = "49",

pages = "54--62",

journal = "Computer",

issn = "0018-9162",

publisher = "IEEE Computer Society",

}

TY - GEN

T1 - A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining

AU - Xu, Lei

AU - Jiang, Chunxiao

AU - Chen, Yan

AU - Wang, Jian

AU - Ren, Yong

PY - 2016/2

Y1 - 2016/2

N2 - To protect sensitive information in mined data, researchers need a way to organize a variety of ongoing work. The Rampart framework categorizes protection approaches and encourages interdisciplinary solutions to the growing variety of privacy problems associated with knowledge discovery from data.

AB - To protect sensitive information in mined data, researchers need a way to organize a variety of ongoing work. The Rampart framework categorizes protection approaches and encourages interdisciplinary solutions to the growing variety of privacy problems associated with knowledge discovery from data.

KW - big data

KW - big data management

KW - data mining

KW - game theory

KW - information privacy

KW - information security

KW - privacy

KW - provenance

KW - sensitive information

UR - http://www.scopus.com/inward/record.url?scp=84962091312&partnerID=8YFLogxK

U2 - 10.1109/MC.2016.43

DO - 10.1109/MC.2016.43

M3 - Article

AN - SCOPUS:84962091312

SN - 0018-9162

VL - 49

SP - 54

EP - 62

JO - Computer

JF - Computer

ER -

A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this