Quality Evaluation of an Anonymized Dataset

Samuel Fletcher; Md Zahidul Islam

doi:10.1109/ICPR.2014.618

Quality Evaluation of an Anonymized Dataset

Samuel Fletcher, Md Zahidul Islam

Research output: Book chapter/Published conference paper › Conference paper › peer-review

7 Citations (Scopus)

4 Downloads (Pure)

Abstract

In this study we argue that the traditional approachof evaluating information quality in an anonymized (or otherwisemodified) dataset is questionable. We propose a novel and simpleapproach to evaluate the information quality of a dataset, andthereby the quality of anonymization techniques. We carry outexperiments on eleven datasets and the empirical results stronglysupport our arguments. We also present some extensions of ourapproach that provide additional insight into the informationquality of modified data.

Original language	English
Title of host publication	ICPR 2014
Editors	Magnus Borga
Place of Publication	United States
Publisher	IEEE
Pages	3594-3599
Number of pages	6
ISBN (Electronic)	9781479952090
DOIs	https://doi.org/10.1109/ICPR.2014.618
Publication status	Published - 2014
Event	ICPR 2014: International Conference on Pattern Recognition - Stockholm Waterfront, Stockholm, Sweden Duration: 24 Aug 2014 → 28 Aug 2014 http://www.iapr.org/archives/icpr2014/

Conference

Conference	ICPR 2014
Country/Territory	Sweden
City	Stockholm
Period	24/08/14 → 28/08/14
Other	Welcome to the 22nd International Conference on Pattern Recognition in Stockholm, August 2014, hosted by the Swedish Society for Automated Image Analysis (SSBA). ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.
Internet address	http://www.iapr.org/archives/icpr2014/

Access to Document

10.1109/ICPR.2014.618

Cite this

@inproceedings{c48aec45c45c43ae9bb799a22cd65f59,

title = "Quality Evaluation of an Anonymized Dataset",

abstract = "In this study we argue that the traditional approachof evaluating information quality in an anonymized (or otherwisemodified) dataset is questionable. We propose a novel and simpleapproach to evaluate the information quality of a dataset, andthereby the quality of anonymization techniques. We carry outexperiments on eleven datasets and the empirical results stronglysupport our arguments. We also present some extensions of ourapproach that provide additional insight into the informationquality of modified data.",

keywords = "Anonymization, Data mining, Data quality, Information quality, Noise addition, Privacy preserving data mining",

author = "Samuel Fletcher and Islam, {Md Zahidul}",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = United States: IEEE, 2014. editor/s (773b) = Prof Magnus Borga; Event dates (773o) = 24-28 August 2014; Parent title (773t) = International Conference on Pattern Recognition.; ICPR 2014 : International Conference on Pattern Recognition ; Conference date: 24-08-2014 Through 28-08-2014",

year = "2014",

doi = "10.1109/ICPR.2014.618",

language = "English",

pages = "3594--3599",

editor = "Magnus Borga",

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AU - Fletcher, Samuel

AU - Islam, Md Zahidul

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = United States: IEEE, 2014. editor/s (773b) = Prof Magnus Borga; Event dates (773o) = 24-28 August 2014; Parent title (773t) = International Conference on Pattern Recognition.

PY - 2014

Y1 - 2014

N2 - In this study we argue that the traditional approachof evaluating information quality in an anonymized (or otherwisemodified) dataset is questionable. We propose a novel and simpleapproach to evaluate the information quality of a dataset, andthereby the quality of anonymization techniques. We carry outexperiments on eleven datasets and the empirical results stronglysupport our arguments. We also present some extensions of ourapproach that provide additional insight into the informationquality of modified data.

AB - In this study we argue that the traditional approachof evaluating information quality in an anonymized (or otherwisemodified) dataset is questionable. We propose a novel and simpleapproach to evaluate the information quality of a dataset, andthereby the quality of anonymization techniques. We carry outexperiments on eleven datasets and the empirical results stronglysupport our arguments. We also present some extensions of ourapproach that provide additional insight into the informationquality of modified data.

KW - Anonymization

KW - Data mining

KW - Data quality

KW - Information quality

KW - Noise addition

KW - Privacy preserving data mining

U2 - 10.1109/ICPR.2014.618

DO - 10.1109/ICPR.2014.618

M3 - Conference paper

SP - 3594

EP - 3599

BT - ICPR 2014

A2 - Borga, Magnus

PB - IEEE

CY - United States

T2 - ICPR 2014

Y2 - 24 August 2014 through 28 August 2014

ER -