Fuzzy Bio-Cryptography Key Generation

Transcription

1 11 Fuzzy Bio-Cryptography Key Generation HanaaM. A. Salman Computer Science Department,University of Technology, Iraq Abstract:Strength of Cryptographic systems security depends mainly upon the security of the used key, which is leads to generate big key space that is result in forget them easily. One solution to this problem is by using Bio-Cryptography key generation. Bio-Cryptography is the integration between cryptography and biometrics that can function in key release mode or in key generation mode. Insipid of the sensed biometric means the result feature vector for the same person is varies each time. This paper addresses solution for various types of security problems by introducing a fuzzy bio-cryptography key generation in a general form consist of: 1. sensor, 2. feature extraction, 3. Key generator. The key is generating dynamically with the help of fingerprint based cubic spline, which is fast and secure. This key is useable for any type of cryptography. Keywords:Bio-Cryptography Fingerprint Fuzzy Cubic Spline Key Generation 1. Introduction Key generation modes is more secure but it is difficult to apply because of large intra-class variations in The incapability of human users to remember strong biometric data due to absorb the differences inherent in cryptographic keys has been main factor limiting the the measurement of biometric or biometric themselves, security of these systems. Usually users can remember while generating the same key over andover again. only short passwords. Consequently they tend to choose Generally, it requires a technique designed for passwords that are easily guessed by dictionary attacks. particular biometrics used. This constraint could be deal with in a wide range This paper presents a fuzzy bio-cryptographic key of applications by generating strong cryptographic keys generation, by using biometric fingerprint local feature. from biometric data, perhaps in conjunction with the The fuzzy key generation achieved after applying two entry of a password. The effectiveness of this approach stages fuzzy feature extraction followed by cubic spline lies in the fact that they do not require the user to key generation. Fuzzy feature extraction stage achieved remember and can represent a much stronger keys from by applying fuzzy pre-processing for the input passwords. These keys could then be used in a wide fingerprint, minutiae extraction, and post processing, variety of applications, which include access to virtual the key generation stage is achieved by applying cubic private networks, file encryption,and user spline followed by key generation.all the authentication. Bio-cryptography integrates cryptography and implementation work has been done in MATLAB. The biometrics to take advantage of the strengths of both developed system is evaluated on a database with fingerprints from different people. fields. Bio-cryptographic techniques protected secret key by using biometric feature or generating a key from biometric features. In such systems, some public information is stored. Both the secret key and the templates are hidden in public information. However, it is computationally impossible to extract the key or the templates from the public information directly. There are two subcategories of bio-cryptographic techniques [1] as depicted in Figure 1), these are: 1. Key binding: If public information derived from binding the secret key or templates, it is key binding, see [2, 3], and 2. Key generation: If public information generated from templates only, while the secret key comes from the public information, and query it is key generation see [4, 5, and 6]. Figure 1a-b).two modes of combining biometrics with cryptography:a): key release and b): key generation [7]

2 2. Fingerprint Principals The surface of the finger formed so-called a fingerprint, which is formed of set of associated curves represents ridges dark curves) and the valleys bright curves), as depicted in Figure 2). The points on the finger surface where the ridges end or bifurcate formed so-called the Minutiae, as depicted in Figure 3), which is most common, and distinctive representation, from fingerprint local feature for each individual. a) Ending b) Bifurcation Figure 3 a-b). The Ridge [8] 3. Bio-Cryptography Key Generation Biometric based cryptographic key generation transform biometric features to a unique key that it is impossible to be regenerate from biometric features by imposters. The general framework of the Bio- Cryptographic key generation is composed of two phases as depicted in Figure 4). We extract the minutiae points from the offline fingerprint image and used that set of points for generating bio-cryptographic key. The database provided by FVC2004 Fingerprint Verification Competition 2004) [9], is used as a source to the input fingerprint, so no acquisition stage is used. The major steps involved in each phase are presented in the following sub sections. 3.1Biometric Phase The input isfingerprint image passesinthree main steps, for each step has other sub steps to form a biometric feature vector. The sub steps involved in each step are in the following sub section and these are [9]: 1. Pre-processing: a. Histogram Equalization b. Image Binarization c. Image Segmentation 2. Minutiae extraction: a. Image Thinning b. Minutiae Extraction c. Singularity Detection 3. Post processing: a. False minutiae Reduction Fingerprint Image pre-processing a: Histogram Equalization The first step in the minutiae extraction stage is fingerprint image enhancement, which it increases the contrast between ridges and furrows and connects the some of the false broken points of ridges due to insufficient amount of ink or poor quality of sensor input [10]. In this paper, a fuzzy image enhancement is used as presented in algorithm 1) [8], followed by the results as depicted in Figure 5). Figure 2. Fingerprint image [1] [8] Sensed Image Biometric Phase Key Generation Phase Bio-cryptographic key Figure 4. Bio-cryptographic key generation The input to the biometric phase is the sensed image, and the output is the biometric feature vector, while the input to the key generation phase is the biometric feature vector, and the output is the biocryptographic key. Algorithm 1): Fuzzy Image Enhancement Input: fingerprint image Output: enhanced fingerprint image. Process: Step1: Taking image and set membership function for each gray level as: Step1.1: Where L=max gray level Step 1.2: f x, y) = any gray level. Step 1.3: s =variance between gray values Step2: Get new membership function by putting: Step2.1: v x, y) = 2* µx, y)) 2 if µ x, y) 0.5 Step 2.2: v x, y ) = 1-2* 1 - µ x, y )) 2 if 0.5 µx, y) 1 Step3: Get new im age by s etting th e method as: gx,y)=l-s 2log vx, y) Step4: End

3 15 l t ch block, use roximation of = sk. win g fo rm loc k direc ) the same a s the wa y illustrated b a [10]. = w dicar ed n th )+ + ) ) 688

4 O ut: Minutia pointes P cess: Ste 1: calculate inter rid ge dista nce D = )) 689

5 17, ith [10 slate pect t o rota t e M a c al or ding t, n and T [10]: x y θ = gin 690

6 7. Conclusion 1. In this paper we have considered the fuzzy biocryptographic key generation. We have demonstrated the solution to the primary difficulty of variations inherent in measuring biometrics, or in the biometrics themselves, while generating the same key over and over again, which faced the most effective approach from the security point of view initialled biocryptographic key generation, which represents the most effective solution to the difficulty remembering the user's secret key. 2. We have proposed the fuzzy filter concept to overcome the difficulty of intra classes vibration. We have extracted the content based minutiae features using three main stage fuzzy filter pre-processing minutia extractions, postpre-processing. We have generated the key by using locations in the fingerprint image of the content based feature vector of the fingerprint locale feature, and then we have used the Fuzzy cubic spline to draw carved. The control pointes of the Fuzzy cubic spline are sending as a key generation. 3. This approach has eliminated the variations in intra classes variations inherent in measuring biometrics, or in the biometrics themselves, which result in capability of generating the same key over and over again. Acknowledgements The author s would like to thank the International Arab Conference on Information Technology, and CCIS, Zarqa University, Jordan for supporting this publication. References [1] Peter Stavroulakis, Mark Stamp: Handbook of Information and Communication Security. Springer 2010, ISBN [2] A. Juels, M. Wallenberg: A Fuzzy Commitment Scheme, Proc. 6th ACM Conference on Computer and Communications Security, Singapore 1999) pp [3] A. Juels, M. Sudan: A Fuzzy Vault Scheme, Proc. IEEE Int. Symposium on Information Theory, Lau-sanne 2002) p. 408 [4] Y.-J. Chang, W. Zhang, T. Chen: Biometrics Based Cryptographic Key Generation, Proc. IEEE Conference on Multimedia and Expo, Taipei, Vol ) pp [5] C. Vielhauer, R. Stcinmctz, A. Maycrhofcr: Biomctric Hash Based on Statistical Features of Online Signatures, Proc. 16th Int. Conference on Pattern Recognition, Quebec, Vol ) pp [6] Y. Dodis, R. Ostrovsky, L. Reyzin, A. Smith: Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data, Technical Report 235, Cryptology eprint Archive February 2006) [7] G Margarov, M Tolba, Biometrics based secret sharing using fuzzy vault. 7th IntConf on Computer Science and Information Technologies CSIT'09) 2009) [8] Pravesh Kumar Use of Fuzzy Set and Neural Network to Extract Fingerprint. Minutiae Points and Location, thesis, [9] [10] kushal veer singh, application of neural networks in fingerprint identification,2010,thesis [11] Maria Cristina Floreno, Giovanni Novelli, Implementing fuzzy polynomial interpolation FPI) and fuzzy linear regression LFR) ticle/view/426 Associate Prof. Dr. Hanaa M. A. Salman awarded her MSc and her PhD from University of technology Iraq in 2002, 2006 respectively. Currently she is a senior lecturer in computer science and a member of the scientific Committee and Promotion Committee in the Department of Computer Science. Dr. Hanaa has more than 15 years of experience and she supervise of graduate students and preliminary,her research interests include Cryptography, Computer Security, Biometrics, image processing, and Computer graphics. Prof. Dr. Hilal Hadi Saleh awarded his PhD in Information system from Higher Institute in Sofia, Bulgaria in1981. Currently he is a senior lecturer in computer science Department of Computer Science. Dr. Hilal has more than 40 years of experience and he supervise of graduate students and preliminary,his research interests include Cryptography, information system, image processing, and Computer graphics,sstatistics