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Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ© ±×·¡ÇÁ¿¡ ³»Æ÷µÈ ½Ã½ºÅÛ-Â÷¿ø ½Ãºô-ÀúÇ× ½Å·Úµµ ÃßÃâ
¿µ¹®Á¦¸ñ(English Title) Extraction of System-Wide Sybil-Resistant Trust Value embedded in Online Social Network Graph
ÀúÀÚ(Author) ±è°æ¹é   Kyungbaek Kim  
¿ø¹®¼ö·Ïó(Citation) VOL 02 NO. 12 PP. 0533 ~ 0540 (2013. 12)
Çѱ۳»¿ë
(Korean Abstract)
ÀÎÅͳÝÀÇ ¹ß´ÞÀÇ ÁÖ¿ä ¿äÀÎ Áß ÇϳªÀÎ ÀÍ¸í¼ºÀº ´Ù¼ö »ç¿ëÀÚµéÀÇ ÀÚÀ¯·Î¿î °³ÀÎ ÀÇ»ç Ç¥ÇöÀ» µµ¿Í ´Ù¾çÇÑ ÀÎÅÍ³Ý ±â¹Ý ºÐ»ê½Ã½ºÅÛÀ» È°¼ºÈ­ Çϴµ¥ ÀÖ¾î Å« µµ¿òÀÌ µÇ¾î ¿Ô´Ù. ÇÏÁö¸¸, ÀÍ¸í¼ºÀº °³ÀÎÀÇ Á¤º¸°¡ ¿ÜºÎ·Î ¾Ë·ÁÁöÁö ¾Ê´Â ´Ù´Â Á¡ ¶§¹®¿¡ ¾Ç¿ëµÉ ¼ÒÁöµµ ´ÙºÐÇÏ´Ù. ƯÈ÷ ºÐ»ê½Ã ½ºÅÛÀº ÇÑ ¸íÀÇ ¾ÇÀÇÀûÀÎ »ç¿ëÀÚ°¡ ´Ù¼öÀÇ °¡Â¥ ½ÅºÐÀ» »ý¼ºÇÏ°í Á¶Á¤ÇÏ´Â ½Ãºô ¾îÅÃ(Sybil Attack)¿¡ ¸Å¿ì Ãë¾àÇÏ°Ô µÈ´Ù. ½Ãºô ¾îÅÃÀ» ¸·±â À§Çؼ­ ºÐ»ê½Ã½ºÅÛ »ó¿¡¼­ ½ÅºÐ »ý¼º ÀÛ¾÷ÀÇ º¹Àâµµ¸¦ ³ôÀÌ´Â ¹æ½ÄÀ̳ª ½Ã½ºÅÛ»óÀÇ ½ÅºÐ°ú Çö½Ç»óÀÇ ½ÅºÐÀÇ ¿¬°á °í¸®¸¦ ¸¸µå´Â ¹æ¹ýÀ» »ý°¢ ÇÒ ¼ö ÀÖ´Ù. ÇÏÁö¸¸ º¹Àâµµ¸¦ ³ôÀÌ´Â ¹æ½ÄÀº °¡Â¥ ½ÅºÐÀÌ ¸¸µé¾îÁö´Â ½Ã°£À» ´Ã¸®´Â È¿°ú¸¸ ÀÖÀ» »Ó, ÀÏ´Ü °¡Â¥ ½ÅºÐÀÌ ¸¸µé¾îÁø ÀÌÈÄ¿¡ ´ëÇÑ ´ëÀÀ¹ýÀÌ ºÎÁ·ÇÏ´Ù. ¶ÇÇÑ, Çö½Ç»óÀÇ ½ÅºÐ°úÀÇ ¿¬°áÀ» »ç¿ëÇÒ °æ¿ì ¿Â¶óÀÎ »ç¿ëÀÚÀÇ ÀÍ¸í¼ºÀÌ ÈѼմçÇÒ ¿ì·Á°¡ ÀÖ´Ù. ÃÖ±Ù ¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ©ÀÇ ´ëÁßÈ­¿Í ÇÔ²² ¼Ò¼È ³×Æ®¿öÅ© ±×·¡ÇÁ Á¤º¸¸¦ »ç¿ëÇØ ½Ãºô ¾îÅÿ¡ ´ëÀÀÇϱâ À§ÇÑ ±â¹ýµéÀÌ ¿¬±¸µÇ°í ÀÖ´Ù. ÀÌ ³í¹®¿¡¼­´Â ¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ© ±×·¡ÇÁ¿¡ ³»Æ÷µÈ Ư¼ºÀ» ÀÌ¿ëÇØ ÀÓÀÇÀÇ »ç¿ëÀÚ¿¡ ´ëÇÑ ½Ã½ºÅÛ Â÷¿ø ½Ãºô-ÀúÇ× ½Å·Úµµ(System-wide Sybil-resistant trust value) ÃßÃâ ¹æ¹ýÀ» Á¦¾È ÇÑ´Ù. Á¦¾ÈÇÏ´Â ±â¹ýÀº ¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ© Àüü ±×·¡ÇÁ¸¦ ÀÌÇØ ÇÒ ¼ö ÀÖ´Â ¼­ºñ½º Á¦°øÀÚµéÀ» À§ÇÑ ¹æ¹ýÀ¸·Î, »ùÇøµ ¹× ÆǴܹæ¹ý¿¡ µû¶ó 3°¡Áö Á¾·ùÀÇ ¼¼ºÎ ±â¹ýµéÀ» Á¦¾ÈÇÑ´Ù. Facebook¿¡¼­ ÃßÃâÇÑ ¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ© »ùÇà ±×·¡ÇÁ¸¦ ÀÌ¿ëÇÏ¿© Á¦¾ÈµÈ ±â¹ýµéÀÇ ¼º´ÉÀ» ºÐ¼® ¹× ºñ±³ ÇÑ´Ù. ¶ÇÇÑ ½Ãºô ¾îÅÃÀÇ Æ¯¼ºÀ» ÀÌÇØÇϱâ À§ÇØ ¼­·Î ´Ù¸¥ ³ëµå Ư¼ºÀ» °¡Áö´Â ³ëµåµéÀÌ ½Ãºô ¾îÅÿ¡ ÀÇÇØ ¹Þ´Â ¿µÇâÀ» ºÐ¼®ÇÑ´Ù.
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(English Abstract)
Anonymity is the one of main reasons for substantial improvement of Internet. It encourages various users to express their opinion freely and helps Internet based distributed systems vitalize. But, anonymity can cause unexpected threats because personal information of an online user is hidden. Especially, distributed systems are threatened by Sybil attack, where one malicious user creates and manages multiple fake online identities. To prevent Sybil attack, the traditional solutions include increasing the complexity of identity generation and mapping online identities to real-world identities. But, even though the high complexity of identity generation increases the generation cost of Sybil identities, eventually they are generated and there is no further way to suppress their activity. Also, the mapping between online identities and real identities may cause high possibility of losing anonymity. Recently, some methods using online social network to prevent Sybil attack are researched. In this paper, a new method is proposed for extracting a user¡¯s system-wide Sybil-resistant trust value by using the properties embedded in online social network graphs. The proposed method can be categorized into 3 types based on sampling and decision strategies. By using graphs sampled from Facebook, the performance of the 3 types of the proposed method is evaluated. Moreover, the impact of Sybil attack on nodes with different characteristics is evaluated in order to understand the behavior of Sybil attack.
Å°¿öµå(Keyword) ½Ãºô ¾îÅà  ½Ã½ºÅÛ-Â÷¿ø ½Ãºô-ÀúÇ× ½Å·Úµµ   ÆнºÆ® ¹Í½Ì ±×·¡ÇÁ   ¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ©   Sybil Attack   Sybil-Resistant Trust Value   Fast Mixing Graph   Online Social Network  
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