IEICE Transactions on Communications
Online ISSN : 1745-1345
Print ISSN : 0916-8516
Regular Section
Estimating Korean Residence Registration Numbers from Public Information on SNS
Daeseon CHOIYounho LEEYongsu PARKSeokhyun KIM
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2015 Volume E98.B Issue 4 Pages 565-574


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.

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© 2015 The Institute of Electronics, Information and Communication Engineers
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