Accurate synthetic generation of realistic personal information

Peter Christen; Agus Pudjijono

doi:10.1007/978-3-642-01307-2_47

Accurate synthetic generation of realistic personal information

Peter Christen^*, Agus Pudjijono

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

57 Citations (Scopus)

Abstract

A large portion of data collected by many organisations today is about people, and often contains personal identifying information, such as names and addresses. Privacy and confidentiality are of great concern when such data is being shared between organisations or made publicly available. Research in (privacy-preserving) data mining and data linkage is suffering from a lack of publicly available real-world data sets that contain personal information, and therefore experimental evaluations can be difficult to conduct. In order to overcome this problem, we have developed a data generator that allows flexible creation of synthetic data containing personal information with realistic characteristics, such as frequency distributions, attribute dependencies, and error probabilities. Our generator significantly improves earlier approaches, and allows the generation of data for individuals, families and households.

Original language	English
Title of host publication	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages	507-514
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-01307-2_47
Publication status	Published - 2009
Event	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand Duration: 27 Apr 2009 → 30 Apr 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5476 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/Territory	Thailand
City	Bangkok
Period	27/04/09 → 30/04/09

Access to Document

10.1007/978-3-642-01307-2_47

Cite this

Christen, P., & Pudjijono, A. (2009). Accurate synthetic generation of realistic personal information. In 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 (pp. 507-514). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI). https://doi.org/10.1007/978-3-642-01307-2_47

@inproceedings{743f8ae0821e4ebba0d4a11adf72de4a,

title = "Accurate synthetic generation of realistic personal information",

abstract = "A large portion of data collected by many organisations today is about people, and often contains personal identifying information, such as names and addresses. Privacy and confidentiality are of great concern when such data is being shared between organisations or made publicly available. Research in (privacy-preserving) data mining and data linkage is suffering from a lack of publicly available real-world data sets that contain personal information, and therefore experimental evaluations can be difficult to conduct. In order to overcome this problem, we have developed a data generator that allows flexible creation of synthetic data containing personal information with realistic characteristics, such as frequency distributions, attribute dependencies, and error probabilities. Our generator significantly improves earlier approaches, and allows the generation of data for individuals, families and households.",

keywords = "Artificial data, Data linkage, Data matching, Data mining pre-processing, Privacy",

author = "Peter Christen and Agus Pudjijono",

year = "2009",

doi = "10.1007/978-3-642-01307-2_47",

language = "English",

isbn = "3642013066",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "507--514",

booktitle = "13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009",

note = "13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 ; Conference date: 27-04-2009 Through 30-04-2009",

}

Christen, P & Pudjijono, A 2009, Accurate synthetic generation of realistic personal information. in 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5476 LNAI, pp. 507-514, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, Bangkok, Thailand, 27/04/09. https://doi.org/10.1007/978-3-642-01307-2_47

Accurate synthetic generation of realistic personal information. / Christen, Peter; Pudjijono, Agus.
13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 507-514 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Accurate synthetic generation of realistic personal information

AU - Christen, Peter

AU - Pudjijono, Agus

PY - 2009

Y1 - 2009

N2 - A large portion of data collected by many organisations today is about people, and often contains personal identifying information, such as names and addresses. Privacy and confidentiality are of great concern when such data is being shared between organisations or made publicly available. Research in (privacy-preserving) data mining and data linkage is suffering from a lack of publicly available real-world data sets that contain personal information, and therefore experimental evaluations can be difficult to conduct. In order to overcome this problem, we have developed a data generator that allows flexible creation of synthetic data containing personal information with realistic characteristics, such as frequency distributions, attribute dependencies, and error probabilities. Our generator significantly improves earlier approaches, and allows the generation of data for individuals, families and households.

AB - A large portion of data collected by many organisations today is about people, and often contains personal identifying information, such as names and addresses. Privacy and confidentiality are of great concern when such data is being shared between organisations or made publicly available. Research in (privacy-preserving) data mining and data linkage is suffering from a lack of publicly available real-world data sets that contain personal information, and therefore experimental evaluations can be difficult to conduct. In order to overcome this problem, we have developed a data generator that allows flexible creation of synthetic data containing personal information with realistic characteristics, such as frequency distributions, attribute dependencies, and error probabilities. Our generator significantly improves earlier approaches, and allows the generation of data for individuals, families and households.

KW - Artificial data

KW - Data linkage

KW - Data matching

KW - Data mining pre-processing

KW - Privacy

UR - http://www.scopus.com/inward/record.url?scp=67650700151&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-01307-2_47

DO - 10.1007/978-3-642-01307-2_47

M3 - Conference contribution

SN - 3642013066

SN - 9783642013065

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 507

EP - 514

BT - 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009

T2 - 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009

Y2 - 27 April 2009 through 30 April 2009

ER -

Accurate synthetic generation of realistic personal information

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this