Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
ÇѱÛÁ¦¸ñ(Korean Title) |
°áÁ¤Æ®¸® ±â¹ÝÀÇ ±â°èÇнÀÀ» ÀÌ¿ëÇÑ µ¿Àû µ¥ÀÌÅÍ¿¡ ´ëÇÑ ÀçÀ͸íȱâ¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Re-anonymization Technique for Dynamic Data Using Decision Tree Based Machine Learning |
ÀúÀÚ(Author) |
±è¿µ±â
È«Ãæ¼±
Young Ki Kim
Choong Seon Hong
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¿ø¹®¼ö·Ïó(Citation) |
VOL 44 NO. 01 PP. 0021 ~ 0026 (2017. 01) |
Çѱ۳»¿ë (Korean Abstract) |
»ç¹°ÀÎÅͳÝ, Ŭ¶ó¿ìµå ÄÄÇ»ÆÃ, ºòµ¥ÀÌÅÍ µî »õ·Î¿î ±â¼úÀÇ µµÀÔÀ¸·Î ó¸®ÇÏ´Â µ¥ÀÌÅÍÀÇ Á¾·ù¿Í ¾çÀÌ Áõ°¡Çϸé¼, °³ÀÎÀÇ ¹Î°¨ÇÑ Á¤º¸°¡ À¯ÃâµÇ´Â °Í¿¡ ´ëÇÑ º¸¾ÈÀ̽´°¡ ´õ¿í Áß¿ä½ÃµÇ°í ÀÖ´Ù. ¹Î°¨Á¤º¸¸¦ º¸È£Çϱâ À§ÇÑ ¹æ¹ýÀ¸·Î µ¥ÀÌÅÍ¿¡ Æ÷ÇÔµÈ °³ÀÎÁ¤º¸¸¦ °ø°³ ¶Ç´Â ¹èÆ÷Çϱâ Àü¿¡ ÀϺθ¦ »èÁ¦Çϰųª ¾Ë¾Æº¼ ¼ö ¾ø´Â ÇüÅ·Πº¯È¯ÇÏ´Â À͸íÈ ±â¹ýÀ» »ç¿ëÇÑ´Ù. ±×·¯³ª Áؽĺ°ÀÚÀÇ ÀϹÝÈ ¼öÁØÀ» °èÃþÈÇÏ¿© À͸íȸ¦ ¼öÇàÇÏ´Â ±âÁ¸ÀÇ ¹æ¹ýÀº µ¥ÀÌÅÍ Å×À̺íÀÇ ·¹Äڵ尡 Ãß°¡ ¶Ç´Â »èÁ¦µÇ¾î k-ÀÍ¸í¼ºÀ» ¸¸Á·ÇÏÁö ¸øÇÏ´Â °æ¿ì¿¡ ´õ ³ôÀº ÀϹÝÈ ¼öÁØÀ» ÇÊ¿ä·Î ÇÑ´Ù. ÀÌ¿Í °°Àº °úÁ¤À¸·Î ÀÎÇÑ Á¤º¸ÀÇ ¼Õ½ÇÀÌ ºÒ°¡ÇÇÇϸç ÀÌ´Â µ¥ÀÌÅÍÀÇ À¯¿ë¼ºÀ» ÀúÇØÇÏ´Â ¿ä¼ÒÀÌ´Ù. µû¶ó¼ º» ³í¹®¿¡¼´Â °áÁ¤ Æ®¸® ±â¹ÝÀÇ ±â°èÇнÀÀ» Àû¿ëÇÏ¿© ±âÁ¸ÀÇ À͸íÈ ¹æ¹ýÀÇ Á¤º¸ ¼Õ½ÇÀ» ÃÖ¼ÒÈÇÏ¿© µ¥ÀÌÅÍÀÇ À¯¿ë¼ºÀ» Çâ»ó½ÃÅ°´Â À͸íÈ ±â¹ýÀ» Á¦¾ÈÇÑ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.
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Å°¿öµå(Keyword) |
¹Î°¨Á¤º¸
À͸íÈ
k-ÀÍ¸í¼º
°áÁ¤Æ®¸®
±â°èÇнÀ
sensitive information
anonymization
k-anonymity
decision tree
machine learning
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ÆÄÀÏ÷ºÎ |
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