Types of Biometric Systems, PETs and Threats for WSN

This article discusses various types of biometric systems such as face recognition, fingerprint recognition, voice recognition, iris recognition and hand geometry systems. It also covers privacy enhancing technologies (PETs) like inaccurate online data, communication anonymizer and Enhanced Privacy ID (EPID). Additionally, it talks about the threats for wireless sensor networks (WSN) such as denial of services, tampering and injection of erroneous data with recommendations to overcome them.

Running head: IT RISK MANAGEMENT
IT Risk Management
Name of the Student
Name of the University
Author’s Note:

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Table of Contents
Question 1..................................................................................................................................2
Question 2..................................................................................................................................8
Question 3................................................................................................................................10
References................................................................................................................................12

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Question 1
Various Types of the Biometric System with Advantages, Disadvantages and Examples
There are several types of biometric authentication system, which are:
i) Face Recognition Systems: This is the first and the most important type of
biometric authentication system (Bhagavatula et al., 2015). It is responsible for helping in the
significant verification or even better identification of that specific individual from the
various digitalized images; either by simply analysis of those images or by comparing the
patterns in the face image.
There are some of the major techniques, which are eventually used for this face
recognition system, which are:
a) Geometry Based: The very first technique of the face recognition system is based
on the face geometry. The fewer relative poses and the edge detection can be easily and
promptly executed by taking the help of this technique (Awasthi & Srivastava, 2013). Three
distinct parts of a face are being considered here for face matching like eyes, mouth and nose.
b) Feature Based: This particular technique is responsible for matching eyes, lips and
nose of the particular individual. These three parts are used as the typical predominant
aspects for the major purpose of finding the appropriate face.
ii) Fingerprint Recognition Systems: The second important type of the biometric
authentication system is the fingerprint recognition system (Sayed et al., 2013). This
specified biometric system subsequently takes down the respective fingerprint images of the
individual and next the basic features such as loops, whorls and arches are recorded only after
undertaking the various outlines like furrow, edge and minutiae.

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There are some of the major techniques, which are eventually used for this fingerprint
recognition system, which are:
a) Correlation: The very first technique of the fingerprint recognition system is the
correlation that could overlay the two typical images of fingerprints by simply associating
within the respective equivalent pixels (Klonovs et al., 2013).
b) Minutiae: The second significant technique of this fingerprint recognition system
is minutiae, which has the ability of storing the plane, when the point set is being included
within the input output minutiae.
iii) Voice Recognition Systems: This is the third and another important type of
biometric authentication system (Bhatt & Santhanam, 2013). The voice recognition systems
could be solely utilized for the proper production of speech patterns by simply combining
both the physiological as well as behavioural features. These specific physiological and
behavioural features could be eventually by proper speech technology processing.
There are some of the major techniques, which are eventually used for this voice
recognition system, which are:
a) Automated Detection of the Voice Signal: The first technique of a voice
recognition system helps in easy and automatic detection of various signals of voices of the
individuals (Abo-Zahhad, Ahmed & Abbas, 2014).
b) Feature Extractions: This is the next technique for the biometric type of voice
recognition systems. Here, the features of the voice of any specific person are being extracted
for proper identification and verification.
iv) Iris Recognition System: The fourth and one of the most important kind of this
biometric authentication system is these iris recognition systems (Chen, Pande & Mohapatra,

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2014). It helps in the perfect identification as well as verification of the authenticated persons,
which is eventually done on the basis of the single pattern within their ring shaped region in a
person’s eyes’ pupil. This particular technique even helps in checking and verifying the
colours of their eyes.
There are some of the major techniques, which are eventually used for this iris
recognition system, which are:
a) Normalization: This is the first and the foremost technique for the specified iris
recognition systems (Chaudhry et al., 2015). In normalization technique, a remapping is
being done for their iris regions by taking the core help of non centric, normalized and
polarized representations.
b) Segmentation: This is the second important or vital technique for the iris
recognition systems. Similar to its name, this particular technique helps in perfect
segmentation of the specified iris of the authenticated person to small circular images (Nandi
et al., 2014). Hence, it is much effective for the perfect verification of each and every
individual.
v) Hand Geometry Systems: Another popular and noteworthy type of biometric
authentication system is the hand geometry. In this particular type of biometric system, a
proper verification of the person is done by following a simplified identification process of
various hand shapes (Peng et al., 2014). The hand geometry hence can be properly measured
by taking the core help of each and every dimension and then all of these dimensions are
being compared to the previous measurements.
There are some of the major techniques, which are eventually used for this hand
geometry system, which are:

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a) False Acceptance Rates: This particular technique is responsible for evaluating the
total ratios of all the unauthorized numbers of user with the summation of the attempts made.
b) False Rejection Rates: This is the second technique of hand geometry system,
which is responsible for evaluating the total ratios of all the authorized numbers of user with
the summation of the attempts made (Roth et al., 2013). These authorized users are
eventually rejected by the biometric system.
c) Equal Error Rates: This is the last or the final technique for hand geometry system
that subsequently matches the rate of false acceptance with the rate of false rejection.
The benefits and the disadvantages of three distinct types of biometric authentication
system are given below:
i) Fingerprint Recognition Systems: There are various important and noteworthy
advantages of the biometric authentication system of fingerprint recognition system, which
are:
a) Better Cost Effectiveness: This the major and the most important advantage of the
fingerprint recognition system (Murillo-Escobar et al., 2015). It provides better cost
effectiveness and hence any user can easily afford this system without much complexity.
b) Higher Security: Another important advantage of the fingerprint recognition
system is that it provides higher security to information and data.
c) Simplified Architecture: The next important and noteworthy benefit of this
fingerprint recognition system would be the entire architecture or the structural design is
quite simplified (Sizov, Lee & Kinnunen, 2014).

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In spite of having these advantages, few disadvantages are also present for this
particular system, which are:
a) Inaccuracy in Data: Fingerprint recognition system eventually provides inaccuracy
in the data for its users.
b) Needs Extra Hardware: Fingerprint recognition system needs the proper
deployment of an extra hardware and without this deployment; it is not possible to use this
system (Chun et al., 2014).
The basic application of the fingerprint recognition systems is when the registration as
well as identification of a voter’s card is being done.
ii) Hand Geometry System: There are several major advantages of this particular
biometric system, which are:
a) Cost Effectiveness: This is the first benefit of the hand geometry system would be
that this provides cost effectiveness and thus affording this system is much easier.
b) Easier Data Collection: Data collection is much easier in this particular system of
hand geometry (Nandi et al., 2014).
In spite of having these advantages, few disadvantages are also present for this
particular system, which are:
a) Bulky Datum: The size of the data is extremely large and bulky.
b) Not Used Majorly: Another important demerit of hand geometry system is that it
could not be used majorly by the users.
The example of application for hand geometry is either in school, colleges or offices.

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iii) Iris Recognition Systems: There are several major advantages of this particular
biometric system, which are:
a) Higher Security: Iris recognition system gives higher security for the confidential
data.
b) Accurate Data: This particular system of iris recognition always provides accuracy
in data (Chaudhry et al., 2015).
In spite of having these advantages, few disadvantages are also present for this
particular system, which are:
a) Expensive: Iris recognition is quite costly and thus it is not possible for everyone to
afford it.
b) Obscure: This system is obscured often by lenses and eyelashes and this is the
second demerit of iris recognition.
The example of the application of an iris recognition system is either in army or in
police ground for proper verification.

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Question 2
Three Privacy Enhancing Technologies or PETs with Examples
There are three important PETs or Privacy Enhancing Technologies, which are:
i) Iaccurate Online Data: This is the first and one of the most popular Privacy
Enhancing Technology. Whenever a user creates an account for the MSN, often inaccurate
data is provided to him for security purposes. The wrong data is for name, address, ban
details and contract details (Awasthi & Srivastava, 2013). Next, the identification and the
password of that particular user are substantially published over the Internet. Hence, this
particular user can promptly utilize his account, without even having any issue and moreover,
he is being ensured that all the private data are absolutely secured and they are not at all
shared on Internet. Furthermore, this particular PET also makes that sensitive data or
information preserved within the database and then the data integrity is maintained.
The best example of wrong data online would be that a user is eventually sharing the
forged data online for ensuring that his original data is safe or secured.
ii) Communcation Anonymizer: This particular Privacy Enhancing Technology helps
to hide the original online identities by simply replacing the original identity with the non
traceable identity (Bhagavatula et al., 2015). The communication anonymizer culd be applied
within emails, instant messaging, P2P networking or several others.
The best example of this PET is the disguising of few IP addresses within
anonymising networks.
iii) EPID: The EPID or Enhanced Privacy ID is the basic algorithm of any particular
digital signature, which helps in supporting anonymity. Furthermore, the private signature

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keys and public verification keys are even provided by this EPID. It is created for giving a
specific device to the external party and then identifies this type of device for using the
privacy ID (Sayed et al., 2013). The major advantage of EPID would be that the original
identity could be revealed by this PET and the privacy of the user’s data is not affected.
The best example of this particular PET would be using PKI in the algorithm of
digital signatures.

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Question 3
Wireless Sensor Networks or WSN and the Three Threats for WSN with Proper
Recommendations
The WSNs are collectiveness of few sensors, which is helpful to monitor as well as
record the physical conditions. OSI architecture is being followed in the architecture of WSN.
The architecture and protocol stack consists of the five cross and five other layers (Bhatt &
Santhanam, 2013). The other five layers are physical, data link, network, transport and
application and the cross five layers are power, mobility, task, QoS management and security
management. All of these layers of WSN architecture and protocol stack are quite effective
for the users.
Figure 1: WSN Architecture and Protocol Stack
(Source: Klonovs et al., 2013)

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There are three threats for the WSN architecture, which are:
i) Denial of Services: In this attack, the hacker seeks into machine to make it
unavailable. This occurs within physical layer.
ii) Tampering: Within this attack, the respective adversary compromises the sensor
node to use those nodes for misleading the network activity (Peng et al., 2014).
iii) Injection of Erroneous Data: The datum is routed in an erroneous manner and
thus the last data is replayed.
The recommendations for these three threats are:
i) Denial of Services: Message prioritization is important to jam the spread spectrum
in WSN.
ii) Tampering: Tamper proofing would be effective here.
iii) Injection of Erroneous Data: Cryptography is the best solution for removing this
particular issue.

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References
Abo-Zahhad, M., Ahmed, S. M., & Abbas, S. N. (2014). Biometric authentication based on
PCG and ECG signals: present status and future directions. Signal, Image and Video
Processing, 8(4), 739-751.
Awasthi, A. K., & Srivastava, K. (2013). A biometric authentication scheme for telecare
medicine information systems with nonce. Journal of medical systems, 37(5), 9964.
Bhagavatula, R., Ur, B., Iacovino, K., Kywe, S. M., Cranor, L. F., & Savvides, M. (2015).
Biometric authentication on iphone and android: Usability, perceptions, and
influences on adoption.
Bhatt, S., & Santhanam, T. (2013, February). Keystroke dynamics for biometric
authentication—A survey. In Pattern Recognition, Informatics and Mobile
Engineering (PRIME), 2013 International Conference on (pp. 17-23). IEEE.
Chaudhry, S. A., Mahmood, K., Naqvi, H., & Khan, M. K. (2015). An improved and secure
biometric authentication scheme for telecare medicine information systems based on
elliptic curve cryptography. Journal of Medical Systems, 39(11), 175.
Chen, S., Pande, A., & Mohapatra, P. (2014, June). Sensor-assisted facial recognition: an
enhanced biometric authentication system for smartphones. In Proceedings of the
12th annual international conference on Mobile systems, applications, and
services (pp. 109-122). ACM.
Chun, H., Elmehdwi, Y., Li, F., Bhattacharya, P., & Jiang, W. (2014, June). Outsourceable
two-party privacy-preserving biometric authentication. In Proceedings of the 9th

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ACM symposium on Information, computer and communications security (pp. 401-
412). ACM.
Klonovs, J., Petersen, C. K., Olesen, H., & Hammershoj, A. (2013). ID proof on the go:
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Vehicular Technology Magazine, 8(1), 81-89.
Murillo-Escobar, M. A., Cruz-Hernández, C., Abundiz-Pérez, F., & López-Gutiérrez, R. M.
(2015). A robust embedded biometric authentication system based on fingerprint and
chaotic encryption. Expert Systems with Applications, 42(21), 8198-8211.
Nandi, S., Roy, S., Dansana, J., Karaa, W. B. A., Ray, R., Chowdhury, S. R., ... & Dey, N.
(2014). Cellular automata based encrypted ECG-hash code generation: an application
in inter human biometric authentication system. International Journal of Computer
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Peng, J., El-Latif, A. A. A., Li, Q., & Niu, X. (2014). Multimodal biometric authentication
based on score level fusion of finger biometrics. Optik-International Journal for Light
and Electron Optics, 125(23), 6891-6897.
Roth, J., Liu, X., Ross, A., & Metaxas, D. (2013, June). Biometric authentication via
keystroke sound. In Biometrics (ICB), 2013 International Conference on (pp. 1-8).
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Sayed, B., Traore, I., Woungang, I., & Obaidat, M. S. (2013). Biometric authentication using
mouse gesture dynamics. IEEE Systems Journal, 7(2), 262-274.
Sizov, A., Lee, K. A., & Kinnunen, T. (2014, August). Unifying probabilistic linear
discriminant analysis variants in biometric authentication. In Joint IAPR International

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Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and
Syntactic Pattern Recognition (SSPR) (pp. 464-475). Springer, Berlin, Heidelberg.