Browsing by Author "Lu, Peggy Joy"

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An Privacy-preserving Cross-organizational Authentication/Authorization/Accounting System using Blockchain Technology
(2018-01-01)
Lu, Peggy Joy
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Yeh, Lo-Yao
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Huang, Jiun-Long
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交大名義發表
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資訊工程學系
Thanks to the growth of cloud computing and network technology, different organizations might want to share data and resources between each other. However, cross-organizational authentication systems usually need a central control system, which must be fully trusted. Thus, we use blockchain technology to store the access control list of users due to its tamper-proof and decentralized feature. Our system also provides authentication/authorization/accounting functions by using a virtual coin exe_coin to achieve accounting function. The method of one-way hash chain is used to securely adapt to the transparency feature of blockchain. In authentication system, the transparency may lead to the linkability problem. In our scheme, attackers cannot get the linkability between the transactions and the particular user. To the best of our knowledge, our scheme is the first blockchain-based authentication system with the merits of unlinkability and accounting.
Intelligent Nighttime Video Surveillance Using Multi-Intensity Infrared Illuminator
(2011-01-01)
Lu, Peggy Joy
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Chuang, Jen-Hui
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Lin, Horng-Horng
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資訊工程學系
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Department of Computer Science
In nighttime video surveillance, the image details of far objects are often hard to be identified due to poor illumination conditions while the image regions of near objects may be whitened due to overexposure. To alleviate the two problems simultaneously for nighttime video surveillance, we adopt a new multi-intensity infrared illuminator as a supportive light source to provide multiple illumination levels periodically. By using the illuminator with multiple degrees of illumination power, both far and near objects can be clearly captured. For automatic detection of foreground objects at different distances in the image sequences captured with the multi-intensity infrared illuminator, two foreground object detection methods are proposed in this paper. Experimental results show that the two methods both achieve >90% accuracy in average in foreground object detection while giving different computational complexities.