Fingerprint technology

A biometric is defined as a technology which uses information and data of any person to investigate the identity of any person. In this modern technology biometric technology is growing very fast and many organizations and communities are using this technology for security purpose. This technology depends on particular data or information of any person and this technology will include data programmes in terms of algorithms to identify any person. The main objective of this technology is to increase security in information industries and identify any person by using information systems (Semwal, Singha, Sharma, Chauhan, & Behera, 2017).

There are mainly three types of biometric systems which are following

• Fingerprint
• Hand geometry
• Iris recognition (Fraschini, Hillebrand, Demuru, Didaci, & Marcialis, 2015).

The fingerprint is a type of biometric technology which uses automatic reorganization systems (Nandakumar, & Jain, 2015). This is a very popular biometric system and many mobile organizations are using this technology to increase productivity and efficiency. This is made of a series of grooves and ridges and once a fingerprint is scanned by this technology the information system recognizes information of that person and compare with stored data. This captured fingerprint locates the minutia points and these points are mapped and a line is marked between each point. This map is used to store data or information which is called as minutia template and provides complete information on any person (Marasco, & Ross, 2015).

Examples: Access management, criminal investigation, background check, and point of sale.

• Increased efficiency and productivity
• Improved security systems
• Low-cost system
• Low maintenance cost (Darshan, Premkumar, Nagabhushana, Sharma, Prashanth, & Prasad, 2016).

• Less convenient
• One person can use the number of identities
• Cost of installation is very high
• Less effective

Hand geometry is one of the oldest technologies of biometric which was implemented in year 1980s. This technology is used for public acceptance, for reorganization and identification, and integration capabilities. One of the common limitations of hand geometry is that this technology is not highly unique and many organization and communities are using this technology for recognition and detection process. This technology utilizes the simple concept of recording detecting the thickness, length, and width of any person’s hand and this biometric system use camera to imprisonment an image of the hand. After that statistics capture through a CCD process which involves the highest view of the pointer and measures the distance of hand using CCD technology (Gupta, & Gupta, 2018).

Examples: distance measurement, for identification, and background check.

• Simple and easy to use
• This technology is easy to collect data of a hand
• Considered less intrusive rather than fingerprint system (Klonowski, Plata, & Syga, 2018).

• Cannot be used for identification purpose
• This technology is not ideal for growing adults and children
• Data size is very large as compared to fingerprint (Jain, Arora, Cao, Best & Bhatnagar, 2017).

Iris recognition is one of the latest versions of the biometric system and around 1000 ATMs in Montreal and Chicago are using this advanced technology. Iris recognition uses pattern technique which is completely based on high determination and alteration free duplicate of people eyes. This technology takes a high-resolution image of human eyes and makes a pattern which is compared with stored data in information systems. This technology reduces the drawbacks of fingerprint biometric and uses a digital camera to capture human eyes after that this image compare with biometric database and recognize person eyes (Gupta, Srivastava, & Gupta, 2016).

Examples: for verification purpose, and security purpose.

• Accuracy
• Distance
• Scalability
• Very easy to use
• Fast process
• More efficient

• More expensive
• Very costly
• Some negative connotations

Privacy enhancing technologies refer to a process which is used as a security system and also protect data or information of human-computer systems. This is modern technology provide a platform to protect data of individual personal information systems. PET is defined as an ICT technology which is used to protect the informational privacy through minimizing their personal data without losing their personal information.  The main purpose of this technology is to protect human personal data by using personally identifiable information systems. In this advanced generation, there are many hackers and cyber risks that can encrypt human personal data and also block their computer system. Therefore to reduce security risks and cyber risks PET is used which uses online services and protect their personal data’s (Acquisti, Brandimarte & Loewenstein, 2015).

Hand Geometry

There are many organizations which are using PETs to reduce security risks and protect personal data by another person. The main application of Privacy enhancing technology is in the information and technology industries to protect human personal information’s. There are many examples of PETs such as announcement anonymizers, communal bogus operational accounts, admission to individual files, and improved privacy ID. In the last few years, the use of the internet is growing rapidly in all countries and many people use the internet for suffering in which they share their personal information through online communities. When any person communicates with another person through online websites than they lost their personal information therefore to reduce this type of security problems PETs are used (Kokolakis, 2017). New users of online websites do not realize that when they use online websites, and social media websites and send emails which can be easily hacked and can be encrypted. Cyber-attack is one of the biggest problems in the information industry because hacker blocks human-computer systems by sending spam emails and messages after that they enter into computer and mobile devices and encrypt all personal files. These type of problem can be reduced by PETs technologies because this technology block spam and fraud emails and protect human personal data and information.

There are many tools are used in this technology such as SSL, encryption method, security tools to protect personal data, and various network systems. It is observed that PETs can reduce security risks by 60% and around 70% of people are using this technology for security purpose. There are mainly three types of PETs which are following

• Web-based PETs
• Network-based PETs
• Personal PETs

Examples: In industries to reduce security risks, use in mobiles to protect personal files and reduce cyber-attacks.

Encryption is one of the biggest changes in the information system and it reduces many cyber risks and security risks. This technology is used in PETs to protect human personal information from another person. Encryption is also used in a telecommunication system to protect transmitted data from the transmitter. In which data is converted into particular code which can be read by users and at when any information transfer from one location to another than this technology is used.

This tool is used into PET to enable the customer to interact anonymously and this provides a unique IP address to every user. There are many users which are using a proxy server as an anonymity tool which can reduce the problem of hacking. The main drawback of this tool is that it is not used for online transaction and it is the very expensive process. There are mainly three types of anonymity tools which are following

• Property managing
• Autonomy-enhancing
• Seclusion enhancing

A wireless sensor network is defined as a wireless network which is used to monitor physical and environmental conditions. This is an advanced technology of wired network in which data or information can be transferred from one location to another without using any electric cables or wires. The main advantage of a wireless network is that it can be sued for long distance communication and many organizations are using this technology for security purpose. There are many applications of wireless sensor networks such as monitoring air and water, monitor and control industrial machines, asset tracking, and monitoring of building structures (Han, Liu, Jiang, Shu, & Hancke, 2017).

Iris Recognition

The most popular construction of wireless sensor system is the OSI model and this model involves five layer and 3 cross layers. Wireless sensor networks require five layers such as transport coating, data link layer, application layer, network layer, physical layer and three cross layers such as task management plane, power management plan, and mobility management flat (Kurt,  Yildiz,  Yigit, Tavli, & Gungor, 2017).

This type of layer is used for traffic management and uses numerous software to convert the data signal into information. There are many applications of wireless networks in various fields such as in medical, agricultural, environment, and military.

There are main two Strengths of transport layer such as provide congestion avoidance, and it is the very reliable process. These types of network protocols utilize dissimilar mechanisms for loss recovery. The transport layer can be divided into two part such as a packet driven and event-driven.

The main purpose of this layer is to provide routing for wireless networks and there are many tasks performed by this layer such as partial; memory, buffers, power conserving and self-organized. There are many types of available protocols of this layer which are following

• Flat routing
• Query-driven
• Event-driven
• Hierarchal routing
• Time-driven

Data link layer is used to detect multiplexing data frames, MAC, control data errors, and for data streams.

This type of layer provides an edge for sending a bit of stream and it is also responsible for the generation of the carrier signal, modulation, signal detection and data encryption. IEEE 802.15.4 is one of the best wireless networks with low cost and power consumption (Han, Liu, Jiang, Shu, & Hancke, 2017).

Denial of Service Attacks

In wireless networks, Denial of Service (DOS) is delivered by the unexpected disappointment of hubs or noxious activity. In DOS assault the foe endeavours to subvert, disturb or annihilate a system. DOS assault decreases a system able to give a support of any occasion. The smallest multifaceted DOS attack attempts to debilitate the properties accessible to the fatality hub, by distribution extra pointless letters. There are many DOS in wireless networks such as jamming, tampering, collision, exhaustion, flooding, and DE synchronization (Fadel, Gungor, Nassef,  Akkari, Malik, Almasri, & Akyildiz, 2015).

The wireless networks perform multiple tasks at the time and in which data transfer into the air through which many noise and losses occur in signals which can reduce the efficiency of the input signal. The Sybil assault boards blame accepting plans, for instance, multipath direction-finding disseminates volume and topology provision. Sybil assault attempts to corrupt the uprightness of info safety and advantage use that the dispersed calculation endeavors to accomplish. Any distributed system particularly remote organize is powerless against Sybil assault (Han, Liu, Jiang, Shu, & Hancke, 2017).

A malicious node that acts as a black hole in the scope of the sink draws in the whole movement to be steered through it by publicizing itself as the briefest course. The enemy drops bundles coming from particular sources in the system. This type of threat also impact on long-distance base stations and it produces the high portion of the loss in data or information signals (Han, Liu, Jiang, Shu, & Hancke, 2017).

Therefore these types of threats and risks in WSNs can be reduced by improving security systems and by using an encryption method. The encryption method is very common to reduce security risks and in which signal convert into a form of code which increases the overall performance of signal and reduce threats and risks of wireless networks.

References

Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behaviour in the age of information. Science, 347(6221), 509-514.

Darshan, G. P., Premkumar, H. B., Nagabhushana, H., Sharma, S. C., Prashanth, S. C., & Prasad, B. D. (2016). Effective fingerprint recognition technique using doped yttrium aluminate nano phosphor material. Journal of colloid and interface science, 464, 206-218.

Fadel, E., Gungor, V. C., Nassef, L., Akkari, N., Malik, M. A., Almasri, S., & Akyildiz, I. F. (2015). A survey on wireless sensor networks for the smart grid. Computer Communications, 71, 22-33.

Fraschini, M., Hillebrand, A., Demuru, M., Didaci, L., & Marcialis, G. L. (2015). An EEG-based biometric system using eigenvector centrality in resting state brain networks. IEEE Signal Processing Letters, 22(6), 666-670.

Gupta, P., & Gupta, P. (2018). Multi-biometric Authentication System using Slap Fingerprints, Palm Dorsal Vein and Hand Geometry. IEEE Transactions on Industrial Electronics, 65(12), 9777.

Gupta, P., Srivastava, S., & Gupta, P. (2016). An accurate infrared hand geometry and vein pattern based authentication system. Knowledge-Based Systems, 103, 143-155.

Han, G., Liu, L., Jiang, J., Shu, L., & Hancke, G. (2017). Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 13(1), 135-143.

Jain, A. K., Arora, S. S., Cao, K., Best-Rowden, L., & Bhatnagar, A. (2017). Fingerprint recognition of young children. IEEE Transactions on Information Forensics and Security, 12(7), 1501-1514.

Klonowski, M., Plata, M., & Syga, P. (2018). User authorization based on hand geometry without special equipment. Pattern Recognition, 73, 189-201.

Kokolakis, S. (2017). Privacy attitudes and privacy behavior: A review of current research on the privacy paradox phenomenon. Computers & Security, 64, 122-134.

Kurt, S., Yildiz, H. U., Yigit, M., Tavli, B., & Gungor, V. C. (2017). Packet size optimization in wireless sensor networks for smart grid applications. IEEE Transactions on Industrial Electronics, 64(3), 2392-2401.

Marasco, E., & Ross, A. (2015). A survey on ant spoofing schemes for fingerprint recognition systems. ACM Computing Surveys (CSUR), 47(2), 28.

Nandakumar, K., & Jain, A. K. (2015). Biometric template protection: Bridging the performance gap between theory and practice. IEEE Signal Processing Magazine, 32(5), 88-100.

Semwal, V. B., Singha, J., Sharma, P. K., Chauhan, A., & Behera, B. (2017). An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimedia tools and applications, 76(22), 24457-24475.