IEICE TRANSACTIONS on Communications
Estimating Korean Residence Registration Numbers from Public Information on SNS
Summary :
People expose their personal information on social network services (SNSs). This paper warns of the dangers of this practice by way of an example. We show that the residence registration numbers (RRNs) of many Koreans, which are very important and confidential personal information analogous to social security numbers in the United States, can be estimated solely from the information that they have made open to the public. In our study, we utilized machine learning algorithms to infer information that was then used to extract a part of the RRNs. Consequently, we were able to extract 45.5% of SNS users' RRNs using a machine learning algorithm and brute-force search that did not consume exorbitant amounts of resources.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E98-B No.4 pp.565-574
- Publication Date
- 2015/04/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E98.B.565
- Type of Manuscript
- PAPER
- Category
- Fundamental Theories for Communications
Authors
Daeseon CHOI
ETRI
Younho LEE
SeoulTech
Yongsu PARK
Hanyang Univ.
Seokhyun KIM
ETRI
Keyword
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Daeseon CHOI, Younho LEE, Yongsu PARK, Seokhyun KIM, "Estimating Korean Residence Registration Numbers from Public Information on SNS" in IEICE TRANSACTIONS on Communications,
vol. E98-B, no. 4, pp. 565-574, April 2015, doi: 10.1587/transcom.E98.B.565.
Abstract: People expose their personal information on social network services (SNSs). This paper warns of the dangers of this practice by way of an example. We show that the residence registration numbers (RRNs) of many Koreans, which are very important and confidential personal information analogous to social security numbers in the United States, can be estimated solely from the information that they have made open to the public. In our study, we utilized machine learning algorithms to infer information that was then used to extract a part of the RRNs. Consequently, we were able to extract 45.5% of SNS users' RRNs using a machine learning algorithm and brute-force search that did not consume exorbitant amounts of resources.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E98.B.565/_p
Copy
@ARTICLE{e98-b_4_565,
author={Daeseon CHOI, Younho LEE, Yongsu PARK, Seokhyun KIM, },
journal={IEICE TRANSACTIONS on Communications},
title={Estimating Korean Residence Registration Numbers from Public Information on SNS},
year={2015},
volume={E98-B},
number={4},
pages={565-574},
abstract={People expose their personal information on social network services (SNSs). This paper warns of the dangers of this practice by way of an example. We show that the residence registration numbers (RRNs) of many Koreans, which are very important and confidential personal information analogous to social security numbers in the United States, can be estimated solely from the information that they have made open to the public. In our study, we utilized machine learning algorithms to infer information that was then used to extract a part of the RRNs. Consequently, we were able to extract 45.5% of SNS users' RRNs using a machine learning algorithm and brute-force search that did not consume exorbitant amounts of resources.},
keywords={},
doi={10.1587/transcom.E98.B.565},
ISSN={1745-1345},
month={April},}
Copy
TY - JOUR
TI - Estimating Korean Residence Registration Numbers from Public Information on SNS
T2 - IEICE TRANSACTIONS on Communications
SP - 565
EP - 574
AU - Daeseon CHOI
AU - Younho LEE
AU - Yongsu PARK
AU - Seokhyun KIM
PY - 2015
DO - 10.1587/transcom.E98.B.565
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E98-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2015
AB - People expose their personal information on social network services (SNSs). This paper warns of the dangers of this practice by way of an example. We show that the residence registration numbers (RRNs) of many Koreans, which are very important and confidential personal information analogous to social security numbers in the United States, can be estimated solely from the information that they have made open to the public. In our study, we utilized machine learning algorithms to infer information that was then used to extract a part of the RRNs. Consequently, we were able to extract 45.5% of SNS users' RRNs using a machine learning algorithm and brute-force search that did not consume exorbitant amounts of resources.
ER -
English
