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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
A New Biometrics based Key Exchange and
Deniable Authentication Protocol
K. Saraswathi Dr. R. Balasubramanian
Asst.Proffessor, Department of Computer Science Dean Academic Affairs
Govt Arts College PPG Institute of Technology
Udumalpet, Tirupur, India Coimbatore, India
dharundharsan@rediffmail.com ramamurthybala2@gmail.com
authentication system improves the network security. Some of
Abstract-Wireless Local Area Networks (WLANs) are gaining most widely used biometric are hand geometry, face,
recognition as they are fast, cost effective, supple and easy to use. fingerprint, retina, iris, DNA, signature and voice.
The networks face a serious of issues and challenges in establishing
security to the users of the network. With users accessing networks Biometrics is the science of measuring and statistically
remotely, transmitting data by means of the Internet and carrying analyzing biological data can be used to recognize different
around laptops containing sensitive data, ensuring security is an
body parts like the eyes, fingerprints, facial characteristics,
increasingly multifarious challenge. Therefore it is necessary to make
sure the security of the network users. In order to provide network
voice etc. Thus, it takes security to the next level by not just
security many techniques and systems have been proposed earlier in confining it to authenticating passwords, fingerprint matching
literature. Most of these traditional methods make use of password, techniques [2]. Based on the individual's biometric
smart cards and so on to provide security to the network users. characteristics a biometric system recognizes an individual.
Though these traditional methods are effective in ensuring security The process of a biometric system can be described, in a
they posses some limitations too. The problem with these traditional beginner's manner, by a three-step process. The foremost step
approaches is that there is possibility to forget the password. in this process is collection of the biometric data which is
Moreover, compromised password lead to a fact, that unauthorized formally known as user registration. This step uses different
user can have access to the accounts of the valid user. This paper
sensors, to assist the user in the registration process. The
proposes an approach for network security using biometrics and
deniable authentication protocol. The human biometrics like hand
second step converts and describes the observed data using a
geometry, face, fingerprint, retina, iris, DNA, signature and voice digital representation called a template. This step varies
can be effectively used to ensure the network security. The diverse between modalities and also between vendors. In the third
phases included in this proposed approach are user registration, step, the newly acquired template is compared with one or
fingerprint enhancement, minutiae point extraction, mapping function more previously generated templates stored in a database. The
and deniable authentication protocol. Furthermore, biometric result of this comparison is a “match” or a “non-match” and is
authentication systems can be more convenient for the users since it used for actions such as permitting access, sounding an alarm,
involves no password that might be feared to be forgotten by the etc [15].
network users or key to be lost and therefore a single biometric trait
(e.g., fingerprint) can be used to access several accounts without the
burden of remembering passwords. This proposed paper also
Declaring a match or non-match is based on the obtained
explains some of the fingerprint enhancement techniques to make the template being analogous, but not one and the same, to the
biometric template noise free. Experiments are conducted to evaluate stored template. A threshold determines the measure of
the performance measure of the proposed approach. similarity necessary to result in a match declaration. The
acceptance or rejection of biometric data is completely
Keywords-Biometrics, Cryptography, Data Security, Fingerprint, dependent on the match score falling above or below the
Mapping Function, Minutiae Point, Network Security, User threshold. The threshold is adjustable so that the biometric
Registration. system can be more or less stringent, depending on the
requirements of any given biometric application [15]. Among
I. INTRODUCTION all the biometric techniques, today fingerprints are the most
Accurate, automatic identification and authentication of widely used biometric features for personal identification
users is an elemental problem in network environments. because of their high acceptability, Immutability and
Shared secrets such as personal identification numbers or individuality.
passwords and key devices like smart cards are not just
enough in some cases. This authentication method has This paper proposes a technique to secure the network
traditionally been based on passwords. The problem with these communication using biometric characteristics obtained from
traditional approaches is that there is possibility to forget the the individuals. The biometric characteristic used in this paper
password. Moreover, compromised password lead to a fact, is fingerprint. This proposed paper utilizes image processing
that unauthorized user can have access to the accounts of the technique to extract the biometric measurement called
valid user. The Biometric based user authentication systems minutiae from the user’s fingerprint. The user’s full finger
are highly secured and efficient to use and place total trust on print image is converted and stored as encrypted binary
the authentication server where biometric verification data are template, which is used for authentication by the server of the
stored in a central database [1]. This biometrics based user network. The user’s biometric verification data are first
transformed into a strong secret and is then stored in the
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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
server’s database during registration. The proposed system is
evaluated to determine the performance measures. Network security issues are projected by Benavente et al. in
[6]. The Internet is increasingly becoming a public vehicle for
The remainder of this paper is organized as follows. Section remote operations. Integrating biometric information in the
2 discusses some of the related work proposed earlier in authentication chain explores new problems. Remote virtual
association to biometric based network security. Section 3 identity is starting to play in the way towards an e-Europe, and
describes the proposed approach of providing network security applications for e-government integrate biometrics. Remote
using the biometric characteristics obtained fingerprint. identity of subjects should be unambiguously stated. Several
Section 4 illustrates the performance measures and Section 5 features drive the spread of biometric authentication in
concludes the paper with directions to future work. network applications, in order to provide end-to-end security
across the authentication chain aliveness detection and fake-
II. RELATED WORK resistive methods, network protocols, security infrastructure,
A lot of research has been carried out in the field of integration of biometrics and public key infrastructure (PKI),
establishing network security based on biometric features etc. Their paper proposed a mid-layer interoperable
obtained from individual user [13] [14]. This section of the architecture furnished with a set of generic interfaces and
paper discusses some of the related work proposed earlier in protocol definitions. Their scheme enables a future
association to biometric based network security. introduction of new modules and applications with a minimal
development effort.
In their work [3] Rahman et al. proposed architecture for
secure access of computers inside an organization from a An intelligent fingerprint based security system was
remote location. They used biometrics features and a one-time designed and developed by Suriza et al. in [7]. Traditionally,
password mechanism on top of secure socket layer (SSL) for user authentication is meant to provide an identification
authentication. Moreover they also provided three layers of number or a password that is unique and well protected to
security levels for network communication, and also a assure the overall system security. This type of security
mechanism for secure file accesses based on the security system is very fragile in an area where a higher level of
privileges assigned to various users was proposed. The files to security system is required. Biometrics-based system offers a
be accessed from the server are categorized depending on their new and better approach to user authentication. Biometrics
access privileges and encrypted using a key assigned to each authentication is an automated method whereby an individual
category. The test results of their approach evaluated the identity is confirmed by examining a unique physiological
performance of their proposed approach. trait or behavioral characteristic, such as fingerprint, iris, or
signature, since physiological traits have stable physical
Chung et al. in [4] described a method for biometric based characteristics. The design and development of a fingerprint-
secret key generation for protection mechanism. The binding based security system, comprising the scanner, interface
of the user's identity and biometric feature data to an entity is system, Boltzmann machine neural network and access control
provided by an authority through a digitally signed data system is discussed in this paper. The integration between the
structure called a biometric certificate. Therefore, the main hardware and the software is completed by using Visual Basic
goal (or contribution) of their work is to propose a simple 6 programming language. The results obtained both for the
method for generating biometric digital key with biometric simulation studies and testing of the integrated system with
certificate on fuzzy fingerprint vault mechanism. Biometric real-life physical system have demonstrated the practicality of
digital key from biometric data has many applications such as such system as well as its potential applications in many
automatic identification, user authentication with message fields.
encryption, etc. Therefore, their work analyzed the related
existing scheme and proposed a simplified model where a Ronald in [8] put forth an alternative approach for password
general fuzzy fingerprint vault using biometric certificate with in network security using biometrics. Passwords are the
security consideration. primary means of authenticating network users. However,
network administrators are becoming concerned about the
Dutta et al. in [5] presented a novel method for providing limited security provided by password authentication. Many
network security using biometric and cryptography. They administrators are now concluding that their password-based
proposed a biometrics-based (fingerprint) security systems are not all that secure. User passwords are
Encryption/Decryption Scheme, in which unique key is routinely stolen, forgotten, shared, or intercepted by hackers.
generated using partial portion of combined sender's and Another serious problem is that computer users have become
receiver's fingerprints. From this unique key a random too trusting. They routinely use the same password to enter
sequence is generated, which is used as an asymmetric key for both secure and insecure Web sites as well as their networks at
both Encryption and Decryption. Above unique Key is send work. In response to the proven lack of security provided by
by the sender after watermarking it in sender's fingerprint password authentication, network administrators are replacing
along with Encrypted Message. The computational network passwords with smartcards, biometric authentication,
requirement and network security features are addressed. or a combination of the three. Smart cards are credit card-size
Proposed system has a advantage that for public key, it has not devices that generate random numbers about every minute, in
to search from a database and security is maintained. sync with counterparts on each entry point in the network.
Smart cards work well as long as the card isn't stolen. A better
189 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
choice to ensure network security is the use of biometrics.
Their paper investigated the different biometric techniques
available to determine a person's identity. Also described, Fingerprint Image Normalization
were the criteria for selecting a biometric security solution. In
conclusion, efforts to establish biometric industry standards
(including standard application program interfaces (APIs)) Preprocessing Orientation
were discussed. Estimation
III. PROPOSED APPROACH Minutiae Feature
Biometric cryptosystems [9] join together cryptography and Extraction
biometrics to promote from the strengths of both fields. In Frequency
such systems, while cryptography provides high and Estimation
adjustable security levels, biometrics brings in non-repudiation
and eliminates the must to remember passwords or to carry Mapping Function
tokens etc. In biometric cryptosystems, a cryptographic key is Filtering
generated from the biometric template of a user stored in the
database in such a way that the key cannot be revealed without
a successful biometric authentication. Thinning
The overall architecture of the biometric system to improve
network security is shown in figure 1. The Server maintains a Figure 2 Steps involved in Extracting Feature Point
database where the encrypted minutia template of the user’s A. User Registration
finger print is stored. In this setting, users communicate with This step is popularly known as Enrolment phase. In all the
the server for the principle of user authentication, by rendering security system to enroll as a legitimate user in a service, a
users fingerprint, which is transformed into a long secret user must previously register with the service provider by
detained by the server in its database [1]. ascertaining his/her identity with the provider. Therefore a
scanner is used to scan the fingerprint of the user to reveal
his/her identity for the first time. The finger print image thus
obtained undergoes a series of enhancement steps. This is
described in the following section of this proposed paper.
B. Fingerprint Enhancement
This is very important step in designing a security system
for network security using biometrics. This step comprise of
the subsequent processing on the obtained fingerprint image.
As we all know a fingerprint is made of a series of ridges and
furrows on the surface of the finger. This determines the
Figure 1.Biometric System uniqueness of the individuals fingerprint. No two fingerprints
Figure 2 shows a common idea of obtaining the minutiae can have the same pattern of ridges and furrows. Minutiae
points from biometric feature obtained from the user. The key points are local ridge characteristics that happen at either a
vector is formed based on minutiae points (ridge ending and ridge bifurcation or a ridge ending. The ridges hold the
ridge bifurcation) are encountered in the given finger print information of characteristic features obligatory for minutiae
image [10]. Figure 2 shows various steps involved in the extraction therefore the quality of the ridge structures in a
proposed system for network security using biometrics. fingerprint image turns out to be an important characteristic.
The obtained image is then subjected to image enhancement
techniques to reduce the noise [11]. The following are the
widely used image improvement techniques, normalization,
orientation estimation, local frequency estimation, Gabor
filtering, and thinning.
1 Normalization
The process of standardizing the intensity values in an
image by adjusting the range of gray-level values so that it lies
within a desired range of values is termed as “normalization”.
Moreover the ridge structures in the fingerprint are not
affected as a result of this process. It is carried out to
standardize the dynamic levels of variation in gray-level
values that facilitates the processing of subsequent image
190 http://sites.google.com/site/ijcsis/
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
enhancement stages. Figure 3 shows a image of the fingerprint 4 Thinning
before and after normalization.
The concluding image enhancement pace typically
performed former to minutiae extraction is thinning. Thinning
is a morphological operation that successively erodes away the
foreground pixels until they are one pixel wide. The
application of the thinning algorithm to a fingerprint image
preserves the connectivity of the ridge structures while
forming a skeleton version of the binary image. This skeleton
image is then used in the subsequent extraction of minutiae.
(a) (b) Figure 6 shows the results of thinning to a fingerprint image.
Figure 3. (a) Original Image (b) Image after
normalization
2 Orientation Estimation
The orientation estimation is an essential step in the
enhancement process as the successive Gabor filtering stage
relies on the local orientation in order to successfully enhance
the fingerprint image. Figure 4 (a) and (b) illustrates the
results of orientation estimation and smoothed orientation Figure 6 Thinned Image
estimation of the fingerprint image respectively. In addition to
the orientation image, another important parameter that is used C. Minutiae Feature Extraction
in the construction of the Gabor filter is the local ridge
frequency. The next step is to extract the minutiae from the enhanced
image. The most generally engaged technique of minutiae
extraction is the Crossing Number (CN) concept [12]. This
method engrosses the use of the skeleton image where the
ridge flow pattern is eight-connected. The minutiae are
extracted by scanning the local neighborhood of each ridge
pixel in the image using a 3x3 window. The CN value is then
computed, which is defined as half the sum of the differences
between pairs of adjacent pixels in the eight-neighborhood.
Figure 7 represents the list of minutiae in a fingerprint image.
(a) (b)
Figure 4. (a) Orientation Image (b)
Smoothened Orientation Image
3 Gabor Filtering
Once the ridge orientation and ridge frequency information
has been single-minded, these parameters are used to construct
the even-symmetric Gabor filter. Gabor filters are employed
because they have frequency-selective and orientation
selective properties. These properties allow the filter to be
tuned to give maximal response to ridges at a specific Figure 7. Minutiae
orientation and frequency in the fingerprint image. Therefore, extraction on a
a properly tuned Gabor filter can be used to effectively fingerprint image.
preserve the ridge structures while reducing noise. Figure 5 D. Mapping Function
illustrates the results of using Gabor filter to a fingerprint
image. The coordinate system used to articulate the minutiae point
locations of a fingerprint is a Cartesian coordinate system. The
X and Y coordinate of the minutiae points are in pixel units.
Angles are expressed in standard mathematical format, with
zero degrees to the right and angles increasing in the counter-
clockwise direction. Every minutia can be stored as a binary
string. Each minutiae point can be recorded in 27 bits: 1 bit for
the minutiae type, 9 bits each for minutiae X coordinate and Y
coordinate, and 8 bits for the minutia angle. Thus, the binary
representation of minutiae point is obtained. Suppose Mi = (ti,
Figure 5 Filtered xi, yi, θi) (i = 1 . . . n) are the all extracted minutiae for a
Image fingerprint image. Then these minutiae points can be arranged
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in a list from left to right by ascending X-co-ordinate, if equal MATLAB 7. Some of the minutiae extracted from a sample
by ascending Y-co-ordinate (first X, then Y) as follows: finger print are shown in table 1. In the context of modern
biometrics, these features, called fingerprint minutiae, can be
Mi1Mi2 · · ·Min captured, analyzed, and compared electronically, with
correlations drawn between a live sample and a reference
The result of the feature extraction stage is what is called a sample, as with other biometric technologies. There are two
minutia template (FP). An approximate range on the number requirements for registration using Finger Print. The user
of minutiae found at this stage is from 10 to 80. These are the should obtain the biometric feature from his finger print using
different steps involved in designing a fingerprint based appropriate image processing techniques as one mentioned in
biometric authentication system for network security. the previous section. The second is that the minutia template
should be encrypted with AES 128 bit symmetric cipher and is
E. The Finger Print Hardening Protocol then transmitted to the server for storage in the database, so
that it should not be possible for an outside attacker to
There are two necessities for registration using Finger Print. determine the biometric feature by an exhaustive search either
1. The user should obtain the biometric feature from his finger at the server side or by meet in the middle attack.
print using suitable image processing techniques as one
mentioned in the previous section. Type X Y Direction
2. The minutia template should be encrypted with AES 128 bit 1 35 117 2.93
symmetric cipher and is then transmitted to the server for 1 50 83 2.95
storage in the database, so that it should not be possible for an
0 19 57 2.80
outside attacker to determine the biometric feature by an
exhaustive search either at the server side or by meet in the 0 23 135 0.27
middle attack. Table 1.List of Minutiae
F. The Finger authentication Protocol where 1 represent ridge ending point and 0 represent isolated
point in a fingerprint image. Thus, the minutia can be
To initiate a request for service, user computes his FP1 = expressed as a 4-vector with its elements in order, the type t,
EAES(FP), then the user sends the user ID along with FP1 to the X and Y coordinates (x, y), and the direction θ (Angle
the server. In Lee et al.[16]’s protocol, the authority selects value is a non-negative value between 0 and 179, in units of
two large prime numbers p and q, where q|p-1. Let g be an degree) as shown in table 1. If each minutia is stored with type
element of order q in GF(p). Assume H(...) is a collision-free (1 bit), location (9 bits each for x and y), and direction (8 bits),
hash function with an output of q bits. The secret key of the then each will require 27 bits and the template will require up
sender S is XS ∈ Zq* and YS =gXs mod p is the corresponding to 270 bytes. Then this binary representation is mapped on to a
public key. Similarly, (XR, YR) is the key pair of the receiver finger print hardening protocol for the generation of strong
R, where XR∈Zq* and YR ∈gXR mod p. The symbol “||” is the secret. The performance measures obtained revealed that the
concatenate operator of strings. In this work, Li Gang's[17] proposed method effectively provides network security.
protocol is adopted to implement the authentication protocol. Therefore it can be directly applied to fortify existing standard
Let t1 and t2 be the minutiae template of FP1 and FP2, single-server biometric based security applications. The
Step 1. S chooses t, t1 ∈RZq* and computes r=gt mod p, r1=g analysis for the security of the protocol is based on the
t1
mod p and σ1=H(r||T)XS+t1r1 mod q, where T is a time following assumptions (i) For a cyclic group G, generated by
stamp, and then he sends (r, T, r1,σ1) to R; g, we are given g and gn, n∈N, the challenge is to compute n.
Step 2. R checks whether gσ1 ≡Ys H(r||T) r1 mod p. If not, R (ii) Given g, ga, gb, it is hard to compute gab. Clearly if these
stops. Otherwise, R chooses t2∈RZq* and computes r2 = gt2 assumptions are not satisfied then C, an adversary, can gain
mod p and σ2 H(r||T) XR+t2r2 mod q, and then he sends(r,T,r2, access to the key gab. A compromised session secret does not
σ2) to S; affect the security of the proposed deniable authentication
Step 3. S verifies whether gσ2 ≡YR H(r||T) r2 mod p. protocol.
If not, S stops. Otherwise, S computes The session secret can be derived from k' ≡ YR XsH (M||T)+tr
σ=H(M||T)XS+tr mod q, mod p, where a random t is chosen independently from each
k=(YR)σ mod p session. If an attacker wants to forge the deniable information
and MAC=H(k||M||T||r1||σ1||r2||σ2). Finally, S sends MAC with with the forged message M’ by using the compromised session
M to R; k, the receiver will derive a different session secret from the
Step 4. R computes k'=(YsH(M||T)rr mod p and verifies whether forged information. This is because that the message and its
H(k’||M||T||r1||σ1|| r2||σ2)=MAC. corresponding session secret are interdependent. Thereby, a
If the above equation holds, R accepts it. Otherwise, R rejects compromised session secret does not affect the security of
it and authentication becomes fail. other sessions.
IV. PERFORMANCE MEASURES V. CONCLUSION
This section of the paper explains the performance measures This paper proposes an approach for network security using
of our approach. The fingerprint processing has been done in biometrics. Biometric systems are commonly used to control
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access to physical assets (laboratories, buildings, cash from [14] Alexander P. Pons , and Peter Polak, “Understanding user perspectives
on biometric technology,” Communications of the ACM, vol. 51, no. 9,
ATMs, etc.) or logical information (personal computer pp. 115-118, September 2008.
accounts, secure electronic documents, etc). The human [15] “Biometrics Security Considerations,” Systems and Network Analysis
biometrics like hand geometry, face, fingerprint, retina, iris, Center Information Assurance Directorate, www.nsa.gov/snac.
DNA, signature and voice can be effectively used to ensure [16] W. B. Lee, C. C. Wu, and W. J. Tsaur, “A Novel Authentication
Protocol Using Generalized ElGamal Signature Scheme”, Information
the network security. In biometric cryptosystems, a Sciences, 177, 2007, pp.1376-1381.
cryptographic key is generated from the biometric template of [17] Li Gang1, Xin Xiangjun, Li Wei, "An Enhanced Deniable
a user stored in the database in such a way that the key cannot Authentication Protocol," International Conference on Computational
be revealed without a successful biometric authentication. In Intelligence and Security, Jan 2008
this system, the ideas in the areas of image processing
technique are reused to extract the minutiae from biometric K. Saraswathi received her B.Sc., and M.C.A.,
from Avinashilingam University, Coimbatore,
image. The preprocessing techniques mentioned in this paper TamilNadu, in 1993 and 1996 respectively. She
play an important role in improving the performance of the obtained her M.Phil degree from Bharathiar
proposed biometric based network security system. The University, Coimbatore, TamilNadu, in the year
performance measures obtained revealed that the proposed 2003. Currently she is working as Assistant
Professor, Department of Computer Science,
method effectively provides network security. Therefore it can Government Arts College, Udumalpet. She has the
be directly applied to fortify existing standard single-server long experience of teaching Post graduate and Graduate Students. She is
biometric based security applications. The future works rely currently pursuing her Research in the area of Crypto Systems under
on improving the network security by making use of Mother Teresa University, Kodaikanal, TamilNadu. Her area of interest
includes Biometrics, Cryptography, Network Security, Machine
cancelable biometrics and multimodal biometrics in the Learning and Artificial Intelligence. She has Co-authored a text book on
proposed authentication system. ‘C’ published by Keerthi Publications. She has presented her
publications in various national conferences. She is a member of various
REFERENCES professional bodies.
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