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

Tsinghua University
University of Electronic Science and Technology of China

Research output: Contribution to specialist publication › Article

49 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

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10.1109/MC.2016.43

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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",

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AU - Ren, Yong

PY - 2016/2

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KW - game theory

KW - information privacy

KW - information security

KW - privacy

KW - provenance

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VL - 49

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JO - Computer

JF - Computer

ER -

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

Abstract

Keywords

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