学术讲座【Euclidean Distance Based Encryption: How to Embed Fuzziness in Biometric Based Encryption】

时间：2014年11月13日（星期四）下午15：00

地点：仓山校区成功楼603报告厅

主讲：澳大利亚卧龙岗大学 穆怡教授

主办：数学与计算机科学学院、福建省网络安全与密码技术重点实验室

专家简介：Professor Yi Mu received his PhD from the Australian National University in 1994. He currently is a full professor, Head of School of  Computer Science and Software Engineering and the director of Centre for Computer and Information Security Research at University of Wollongong, Australia. He is currently a MingJiang Scholar of Fujian Normal University. Prior to joining University of Wollongong, he was a senior lecturer in the Department of Computing, Macquarie University. He also worked in Department of Computing and IT, University of Western Sydney as a lecturer. He has been with the University of Wollongong since 2003. His current research interest includes cryptography, network security and computer security. He has published over 350 research papers. He has served as program chair and member of program committee over 200 conferences including ACM CCS, ESORICS, ACISP, AisaCCS, etc. and is currently a member of the steering committees of AsiaCCS, CANS and ProvSec. Professor Yi Mu is the editor-in-chief of International Journal of Applied Cryptography and serves as associate editor for nine other international journals. He is a senior member of the IEEE and a member of the IACR. Further information about Professor Mu can be found at http://www.uow.edu.au/~ymu 。

报告摘要：In this talk, a new encryption notion called Euclidean Distance based Encryption (EDE) will be introduced. In this notion, a ciphertext encrypted with a vector and a threshold value can be decrypted with a private key of another vector, if and only if the Euclidean distance between these two vectors is less than or equal to the threshold value.  Euclidean distance is the underlying technique in the pattern recognition and image processing community for image recognition. The primary applications include biometric identifiers, such as fingerprint, face, hand geometry, vein and iris. In that application, usually the input biometric will not be exactly the same during the enrollment and encryption phases. A construction of this new encryption notion will be presented.