Study on Record Linkage of Anonymizied Data

Hiroaki KIKUCHI; Takayasu YAMAGUCHI; Koki HAMADA; Yuji YAMAOKA; Hidenobu OGURI; Jun SAKUMA

doi:10.1587/transfun.E101.A.19

Study on Record Linkage of Anonymizied Data

Hiroaki KIKUCHI, Takayasu YAMAGUCHI, Koki HAMADA, Yuji YAMAOKA, Hidenobu OGURI, Jun SAKUMA

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Data anonymization is required before a big-data business can run effectively without compromising the privacy of personal information it uses. It is not trivial to choose the best algorithm to anonymize some given data securely for a given purpose. In accurately assessing the risk of data being compromised, there needs to be a balance between utility and security. Therefore, using common pseudo microdata, we propose a competition for the best anonymization and re-identification algorithm. The paper reported the result of the competition and the analysis on the effective of anonymization technique. The competition result reveals that there is a tradeoff between utility and security, and 20.9% records were re-identified in average.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.1 pp.19-28

Publication Date: 2018/01/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E101.A.19

Type of Manuscript: Special Section INVITED PAPER (Special Section on Cryptography and Information Security)

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Authors

Hiroaki KIKUCHI
  Meiji University
Takayasu YAMAGUCHI
  NTT DOCOMO, Inc.
Koki HAMADA
  NTT Secure Platform Laboratories
Yuji YAMAOKA
  FUJITSU LABORATORIES LTD.
Hidenobu OGURI
  NIFTY Corporation
Jun SAKUMA
  University of Tsukuba

Keyword

data privacy, anonymization, re-identification risk, big data

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Hiroaki KIKUCHI, Takayasu YAMAGUCHI, Koki HAMADA, Yuji YAMAOKA, Hidenobu OGURI, Jun SAKUMA, "Study on Record Linkage of Anonymizied Data" in IEICE TRANSACTIONS on Fundamentals, vol. E101-A, no. 1, pp. 19-28, January 2018, doi: 10.1587/transfun.E101.A.19.
Abstract: Data anonymization is required before a big-data business can run effectively without compromising the privacy of personal information it uses. It is not trivial to choose the best algorithm to anonymize some given data securely for a given purpose. In accurately assessing the risk of data being compromised, there needs to be a balance between utility and security. Therefore, using common pseudo microdata, we propose a competition for the best anonymization and re-identification algorithm. The paper reported the result of the competition and the analysis on the effective of anonymization technique. The competition result reveals that there is a tradeoff between utility and security, and 20.9% records were re-identified in average.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.19/_p

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@ARTICLE{e101-a_1_19,
author={Hiroaki KIKUCHI, Takayasu YAMAGUCHI, Koki HAMADA, Yuji YAMAOKA, Hidenobu OGURI, Jun SAKUMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Study on Record Linkage of Anonymizied Data},
year={2018},
volume={E101-A},
number={1},
pages={19-28},
abstract={Data anonymization is required before a big-data business can run effectively without compromising the privacy of personal information it uses. It is not trivial to choose the best algorithm to anonymize some given data securely for a given purpose. In accurately assessing the risk of data being compromised, there needs to be a balance between utility and security. Therefore, using common pseudo microdata, we propose a competition for the best anonymization and re-identification algorithm. The paper reported the result of the competition and the analysis on the effective of anonymization technique. The competition result reveals that there is a tradeoff between utility and security, and 20.9% records were re-identified in average.},
keywords={},
doi={10.1587/transfun.E101.A.19},
ISSN={1745-1337},
month={January},}

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TY - JOUR
TI - Study on Record Linkage of Anonymizied Data
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 19
EP - 28
AU - Hiroaki KIKUCHI
AU - Takayasu YAMAGUCHI
AU - Koki HAMADA
AU - Yuji YAMAOKA
AU - Hidenobu OGURI
AU - Jun SAKUMA
PY - 2018
DO - 10.1587/transfun.E101.A.19
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2018
AB - Data anonymization is required before a big-data business can run effectively without compromising the privacy of personal information it uses. It is not trivial to choose the best algorithm to anonymize some given data securely for a given purpose. In accurately assessing the risk of data being compromised, there needs to be a balance between utility and security. Therefore, using common pseudo microdata, we propose a competition for the best anonymization and re-identification algorithm. The paper reported the result of the competition and the analysis on the effective of anonymization technique. The competition result reveals that there is a tradeoff between utility and security, and 20.9% records were re-identified in average.
ER -