A Method for Data Verification in RFID Using a Physically Unclonable Function

碩士 === 國立東華大學 === 資訊工程學系 === 98 === In recent years, Radio Frequency Identification (RFID) is an advanced and popular technology for automatic identification and data collection. Many companies popularize RFID systems for convenience in management systems. Traditional bar-code systems have been used...

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Bibliographic Details
Main Authors: Ren-Jie Jheng, 鄭仁傑
Other Authors: Ruay-Shiung Chang
Format: Others
Language:en_US
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/13691396716645781634
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Summary:碩士 === 國立東華大學 === 資訊工程學系 === 98 === In recent years, Radio Frequency Identification (RFID) is an advanced and popular technology for automatic identification and data collection. Many companies popularize RFID systems for convenience in management systems. Traditional bar-code systems have been used for many years. With manufacturing automation and different requirements in marketplaces, several drawbacks and inconvenient characteristics of bar-code system surface. It may be replaced by RFID system. In RFID system, we can save more information than in bar-code, and trace or identify an item efficiently. Physically Unclonable Function (PUF) is a function that is embodied in a physical structure. It is one of the latest authentications in semiconductor security. PUF is a silicon biometrics technology, a type of electronic DNA in each IC. It extracts unique “secrets” from each IC, and these secrets are used to authenticate ICs. PUF uses a challenge-response authentication protocol in which one part presents a challenge and another provides a valid answer to be authenticated. In this thesis, we focus on solving the problem of data integrity in RFID using Physically Unclonable Function. Because their responses to massage decoding are different, several models of data integrity can not be used. Compared to CRC, our proposed method reduces the checking time and saves the power. Furthermore, we can adjust dynamically the length of check bit according to the importance of data.