基于指纹特征的新模糊金库算法研究
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摘要
随着人们在互联网上交流活动的日益频繁和普遍，互联网的安全问题越来越受到人们的重视。信息安全问题已经成为网络环境中的关键技术问题，尤其是密码学在网络信息安全中的作用日趋重要。而在密码学中，密钥的安全问题至关重要。有时候，由于密钥过长用户就采用口令来加密保护,但是口令容易受到字典式穷猜测的攻击，安全性很低。生物特征加密技术正是针对传统口令加密算法的弱点而提出的一种密钥保护算法。它是将生物特征和密钥以某种特定的方式结合起来，这样对密钥的存储和管理少了很多麻烦，况且密钥本身又可以对生物特征进行加密和保护，从而增加了攻击者的难度。
在生物特征加密中，指纹是应用最广泛的一种，且发展前景较好。由于模糊金库算法能够很好的解决生物特征的模糊性与密码密钥的精确性之间的矛盾，所以本文在此基础上主要研究基于指纹特征的新模糊金库新算法。具体工作分为以下几个部分：
本文首先对指纹特征识别技术做了深入的研究与分析，对指纹图像预处理等技术做了一定的研究，比较并分析了几种不同的指纹特征提取算法，深入研究了基于细节点的指纹特征提取算法，并且在此基础上做了完整的仿真实验，为指纹密钥生成奠定了良好的基础。
其次，为了提高密钥恢复率，本文在原有模糊金库算法基础上，提出了一种基于指纹及中国剩余定理的新模糊金库算法。加密阶段，引入了两两互素的正整数并将其和密码密钥相互作用，然后和指纹特征相结合生成指纹密钥，这样攻击者必须同时具有加密过程中所引入的正整数和正确的指纹特征点才能攻破金库，从而获得密钥，所以对攻击者来说，难度大大增加。在解密阶段，根据门限思想，由中国剩余定理可获得密钥，经过理论分析和实验验证，密钥的安全性和成功恢复率相对来说较好。
再次，为了降低合法用户的计算复杂度，本文提出了一种基于双重多项式的新指纹模糊金库算法，该算法有效利用了验证数据来验证解锁集合中元素的有效性，它主要是在多项式重构前就去掉解锁集合中的噪声点，这样合法用户的计算复杂性在很大程度上得到了降低。实验结果证明合法用户的计算复杂性有了显著降低。
With the frequency and universality of people communicating with each other on the internet,people have paid more and more attention to the internet security. Information security problem hasbecome a key technical problem in network environment, especially the cryptography plays a moreand more important role in the network information security field. In the cryptography, the keysecurity is very critical. Sometimes, the key is so long that it is difficult for the users to remember,so they use a password to encrypt and protect the key, however, it is vulnerable to dictionary poorguessing attacks, and security is very low. Because of the traditional password encryptionalgorithm’s weaknesses, biometric encryption is proposed to protect the key. It combines thebiometric characteristic with the key in a certain way，it eliminates a lot of trouble of storing andmanaging the key, and the key itself can encrypt and protect the biometric, which increases thedifficulty of the attacker.
In the biometric encryption, fingerprint is most widely used and it has the best developmentprospect. Because the fuzzy vault algorithms can resolve the contradiction which between theambiguity of biometric characteristic and the accuracy of the key, so this paper mainly studies thenew fuzzy vault algorithms based on the fingerprint features and it contains the following severalparts:
Firstly, this paper deeply studies and analyzes the fingerprint identification technology, it doessome researches on the fingerprint image preprocessing techniques, with comparing and analyzingseveral different fingerprint feature extraction algorithms, it studies the minutiae-based fingerprintfeature extraction algorithms, and makes a complete simulation. It has laid a good foundation forthe fingerprint key generation.
Secondly, in order to improve the key recovery rate, this paper proposes a new fuzzy vaultalgorithm based on fingerprint and Chinese Remainder Theorem. In the process of encryption, itintroduces the positive integers which are pair-wise prime, password key is combined with thefingerprint features and the positive integers can be used to generate fingerprint key, so an attackermust have the positive integers which in the encryption process and the correct fingerprint featurepoints that can break the fuzzy vault, then get the key, it has greatly increased the difficulty to theattacker. In the process of decryption, the key can be recovered by using the Chinese RemainderTheorem according to the threshold idea. According to the theoretical analysis and experimentalresults, the key’s safety and the rate of recovery are increased.
Once more, this paper proposes a new fingerprint fuzzy vault algorithm based on double polynomials for reducing the legitimate user’s computational complexity, the algorithm takesadvantage of the verification data to check the validity of elements in the unlocking set, it is mainlyup to removing the noise pointes before the polynomial reconstruction, so the valid user’scomputational complexity can be greatly decreased. Experimental results show that the legitimateusers’computational complexities have been significantly reduced.
在生物特征加密中，指纹是应用最广泛的一种，且发展前景较好。由于模糊金库算法能够很好的解决生物特征的模糊性与密码密钥的精确性之间的矛盾，所以本文在此基础上主要研究基于指纹特征的新模糊金库新算法。具体工作分为以下几个部分：
本文首先对指纹特征识别技术做了深入的研究与分析，对指纹图像预处理等技术做了一定的研究，比较并分析了几种不同的指纹特征提取算法，深入研究了基于细节点的指纹特征提取算法，并且在此基础上做了完整的仿真实验，为指纹密钥生成奠定了良好的基础。
其次，为了提高密钥恢复率，本文在原有模糊金库算法基础上，提出了一种基于指纹及中国剩余定理的新模糊金库算法。加密阶段，引入了两两互素的正整数并将其和密码密钥相互作用，然后和指纹特征相结合生成指纹密钥，这样攻击者必须同时具有加密过程中所引入的正整数和正确的指纹特征点才能攻破金库，从而获得密钥，所以对攻击者来说，难度大大增加。在解密阶段，根据门限思想，由中国剩余定理可获得密钥，经过理论分析和实验验证，密钥的安全性和成功恢复率相对来说较好。
再次，为了降低合法用户的计算复杂度，本文提出了一种基于双重多项式的新指纹模糊金库算法，该算法有效利用了验证数据来验证解锁集合中元素的有效性，它主要是在多项式重构前就去掉解锁集合中的噪声点，这样合法用户的计算复杂性在很大程度上得到了降低。实验结果证明合法用户的计算复杂性有了显著降低。
With the frequency and universality of people communicating with each other on the internet,people have paid more and more attention to the internet security. Information security problem hasbecome a key technical problem in network environment, especially the cryptography plays a moreand more important role in the network information security field. In the cryptography, the keysecurity is very critical. Sometimes, the key is so long that it is difficult for the users to remember,so they use a password to encrypt and protect the key, however, it is vulnerable to dictionary poorguessing attacks, and security is very low. Because of the traditional password encryptionalgorithm’s weaknesses, biometric encryption is proposed to protect the key. It combines thebiometric characteristic with the key in a certain way，it eliminates a lot of trouble of storing andmanaging the key, and the key itself can encrypt and protect the biometric, which increases thedifficulty of the attacker.
In the biometric encryption, fingerprint is most widely used and it has the best developmentprospect. Because the fuzzy vault algorithms can resolve the contradiction which between theambiguity of biometric characteristic and the accuracy of the key, so this paper mainly studies thenew fuzzy vault algorithms based on the fingerprint features and it contains the following severalparts:
Firstly, this paper deeply studies and analyzes the fingerprint identification technology, it doessome researches on the fingerprint image preprocessing techniques, with comparing and analyzingseveral different fingerprint feature extraction algorithms, it studies the minutiae-based fingerprintfeature extraction algorithms, and makes a complete simulation. It has laid a good foundation forthe fingerprint key generation.
Secondly, in order to improve the key recovery rate, this paper proposes a new fuzzy vaultalgorithm based on fingerprint and Chinese Remainder Theorem. In the process of encryption, itintroduces the positive integers which are pair-wise prime, password key is combined with thefingerprint features and the positive integers can be used to generate fingerprint key, so an attackermust have the positive integers which in the encryption process and the correct fingerprint featurepoints that can break the fuzzy vault, then get the key, it has greatly increased the difficulty to theattacker. In the process of decryption, the key can be recovered by using the Chinese RemainderTheorem according to the threshold idea. According to the theoretical analysis and experimentalresults, the key’s safety and the rate of recovery are increased.
Once more, this paper proposes a new fingerprint fuzzy vault algorithm based on double polynomials for reducing the legitimate user’s computational complexity, the algorithm takesadvantage of the verification data to check the validity of elements in the unlocking set, it is mainlyup to removing the noise pointes before the polynomial reconstruction, so the valid user’scomputational complexity can be greatly decreased. Experimental results show that the legitimateusers’computational complexities have been significantly reduced.
引文
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[3] U.Uludag,S.pankanti,P.S.,A.Jain. Biometric Cryptosystem:Issues and Challenges[J]. Proceedings of the IEEE,2004,(92)6:948-960.
[4]苑玮琦，柯丽，白云．生物特征识别技术[M]．北京：科学出版社，2009：3-10．
[5] Adini Y, Moses Y, Ullman S. Face recognition: the problem of compensating for changes in illumination tirection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997.19(7):721-732.
[6] Zhang Z, Ma S, Wu D. The application of neural network and wavelet in human face illumination compensation. 2005. Chongqing, China: Springer Verlag, Heidelberg, D-69121, Germany.
[7] Zhang D. Automated Biometrics: Technologies & Systems. Boston: Technologies & Systems. Boston: Kluwer Academic,2000.
[8] Nicode D, Anil K J, Kanti V M. Matching of palmprints. Pattern Recogn. Lett. ,2002. 23(4): 477-485.
[9]余成波，秦华锋．手指静脉识别技术[M]．北京：清华大学出版社，2009：27-34．
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[11] Okazaki Y, Kyuragi H. Recent progress in user identification technology based on fingerprints. NTTR and D,2002. 51(3): 189-193.
[12] Qi Y, Tian J, Deng X. Genetic algorithm based fingerprint matching algorithm and its application in automated fingerprint recognition system. Ruan Jian Xue Bao/Jpurnal of Software,2000. 11(4):488-493.
[13] Online document: http://www.ipc.on.ca/images/Resources/up-1bio_encryp.pdf
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[28]刘军波，马利庄，聂栋栋，沈丽忠．改进Gabor滤波器的指纹增强算法[J]．计算机工程，2005，31(15)：146-147．
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[30]陈新建，田捷，何余良．基于频域滤波的低质量指纹增强算法[C]．第四届生物特征识别研讨会论文集，北京，2003，12．
[31]徐莹，赵建林等．无透镜傅里叶变换全息图数值再现中的图像处理[J]．光学学报，2004，11(24)：1503-1506．
[32]李昊，傅曦．指纹模式识别系统算法及实现[M]．北京：人民邮电出版社，2008：118-123．
[33]梁光明，孙即祥等．Otsu算法在Canny算子中的应用[J]．国防科技大学学报，2003，25(5)：36-39．
[34]吴留生，陈家新，黎蔚．基于Niblack的手掌静脉图像二值化技术研究[J]．通信技术，2010，1(43)：112-114．
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[2] B. Schneier. Applied Crytography: Protocols, Algorithms, and Source Code in C, 2ndEdition [M]. New York: John Wiley&Sons, 1996: 235-286.
[3] U.Uludag,S.pankanti,P.S.,A.Jain. Biometric Cryptosystem:Issues and Challenges[J]. Proceedings of the IEEE,2004,(92)6:948-960.
[4]苑玮琦，柯丽，白云．生物特征识别技术[M]．北京：科学出版社，2009：3-10．
[5] Adini Y, Moses Y, Ullman S. Face recognition: the problem of compensating for changes in illumination tirection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997.19(7):721-732.
[6] Zhang Z, Ma S, Wu D. The application of neural network and wavelet in human face illumination compensation. 2005. Chongqing, China: Springer Verlag, Heidelberg, D-69121, Germany.
[7] Zhang D. Automated Biometrics: Technologies & Systems. Boston: Technologies & Systems. Boston: Kluwer Academic,2000.
[8] Nicode D, Anil K J, Kanti V M. Matching of palmprints. Pattern Recogn. Lett. ,2002. 23(4): 477-485.
[9]余成波，秦华锋．手指静脉识别技术[M]．北京：清华大学出版社，2009：27-34．
[10] Ma L, et al. Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing, 2004. 13(6): 739-750.
[11] Okazaki Y, Kyuragi H. Recent progress in user identification technology based on fingerprints. NTTR and D,2002. 51(3): 189-193.
[12] Qi Y, Tian J, Deng X. Genetic algorithm based fingerprint matching algorithm and its application in automated fingerprint recognition system. Ruan Jian Xue Bao/Jpurnal of Software,2000. 11(4):488-493.
[13] Online document: http://www.ipc.on.ca/images/Resources/up-1bio_encryp.pdf
[14] U Uludag, S Pankanti, S Prabhakar, et al. Biometric cryptosystems: issues and challenges. Proc. IEEE(Special Issue on Multimedia Security for Digital Rights Management), Jun. 2004,92(6): 948-960.
[15] A.Juels, M.Wattenberg. A fuzzy commitment scheme [C]. In: Proc. of the 6th ACM Conf. Computer and Comm. Security (CCCS). New York: ACM, 1999: 28-36.
[16] A.Juels, M.Sudan. A fuzzy vault scheme [C]. Proc. IEEE Int’l Symp. On Information Theory. Institute of Electrical and Electronics Engineers, 2002: 408.
[17] T.C.Clancy, N.Kiyavash, and D.J.Lin. Secure smartcard based fingerprint authentication[C]. In ACM Workshop on Biometric Methods and Applications, Berkeley, 2003: 45-52.
[18] U.Uludag, S.Pankanti, A.Jain. Fuzzy Vault for Fingerprints[C]. Proc. of the 5th Int’l Conf. on AVBPA. Berlin: Springer-Verlag, 2005: 310-319.
[19] Yang, S.: Verbauwhede, I.M., secure fuzzy vault based fingerprint verification system. Signals, Systems and Computers. 2004. Conference Record of the Thirty-Eighth Asilomar Conference on Volume 1,7-10 Nov.2004 Page(s): 577-581
[20] Y. Dodis, L. Reyzin, and A. Smith. Fuzzy extractors: How to generate strong keys from biometrics and other noisy data. Adv. In Crytology-Eurocrypt 2004. LNCS vol. 3027. Springer-Verlag,2004:523-540.
[21]田捷，杨鑫．生物特征识别理论与应用[M]．北京：清华大学出版社出版社，2009：387-406．
[22] A.K. Jain, Lin Hong, Sharath Pankanti, etal. An Idengtity Authentieation System Using FingerPrints [J]. Proeeedings of the IEEE. 1997, 85(9): 1365-1388.
[23] Mehtre B M,Murthy N N, Kapoor S, Chatterjee B. Segmentation of fingerprint images using the directional images. Pattern Recognition, 1995,28(3): 1678~1683
[24] Hong L, Jian A, Pankanti S, Bolle R. Fingerprint enhancement, applications of computer vision. WACV 96, Proceedings 3rd IEEE Workshop on,1996:202~207
[25] Kim B G, Kim H J, Park D J. New enhancement algorithm for fingerprint images. Proc 16thInternational Conference on Pattern Recognition.2002,3:879~882
[26] Vultipong A, Ukrit W. Fast separable Gabor filter for fingerprint enhancement. In: ICBA 2004:403~409.
[27] Zhang W P, Tang Y Y, You X. Fingerprint enhancement using wavelet transform combined with Gabor filter. International Journal of Pattern Recognition and Artificial Intelligence,2004,18(8):1391~1406.
[28]刘军波，马利庄，聂栋栋，沈丽忠．改进Gabor滤波器的指纹增强算法[J]．计算机工程，2005，31(15)：146-147．
[29] Hong L, Jian A, Pankanti S, Bolle R. Fingerprint enhancement[J]. applications of computer vision. WACV 96, Proceedings 3rd IEEE Workshop on,1996:202-207.
[30]陈新建，田捷，何余良．基于频域滤波的低质量指纹增强算法[C]．第四届生物特征识别研讨会论文集，北京，2003，12．
[31]徐莹，赵建林等．无透镜傅里叶变换全息图数值再现中的图像处理[J]．光学学报，2004，11(24)：1503-1506．
[32]李昊，傅曦．指纹模式识别系统算法及实现[M]．北京：人民邮电出版社，2008：118-123．
[33]梁光明，孙即祥等．Otsu算法在Canny算子中的应用[J]．国防科技大学学报，2003，25(5)：36-39．
[34]吴留生，陈家新，黎蔚．基于Niblack的手掌静脉图像二值化技术研究[J]．通信技术，2010，1(43)：112-114．
[35] Davide, Maltoni, etal. Handbook of fingerprint recognition[M]. New York, Springer-Verlag 2003
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