i-manager Publications

i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 12 Issue 1

Most Cited

Volume 6 Issue 2 April - June 2018

Research Paper

A Simulation Model for Cardless Automated Teller Machine Transactions

O. S. Adewale* , J. O. Mebawondu, O. J. Mebawondu*, M. N. Suleiman****

* Professor, Department of Computer Science, School of Computing, Federal University of Technology (FUTA), Akure, Nigeria.

Principal and Lecturer, Federal Polytechnic, Nasarawa, Nasarawa State, Nigeria.

* Senior Lecturer, Department of Computer Science, School of Information Technology, Federal Polytechnic, Nasarawa State, Nigeria.

**** Lecturer, Federal Polytechnic, Nasarawa, Nasarawa State, Nigeria.

Adewale, O. S., Mebawondu, J. O., Suleimano, M. N., & Mebawondu, J. (2018). A Simulation Model For Cardless Automated Teller Machine Transactions. i-manager's Journal on Digital Signal Processing, 6(2), 1-8. https://doi.org/10.26634/jdp.6.2.15588

Abstract

Cardless automated teller machine (CATM) is an electronic gadget that empowers the bank’s clients to perform monetary transactions such as dispensing cash to their clients, pay bills and transfer of money. The customary approach of an ATM utilizes the use of debit card for its transactions have its limitation. The challenges in utilizing such system are extortion, security and high probability of users forgetting their passwords. In this work, we propose a straightforward paradigm called CATM. The proposed CATM model used a five tuple finite machine. In the proposed platform a thumb print (Biometric) framework is utilized. The platform will improve services to clients, also secure, effective and efficient systems will be achieved.

References

[1]. Ahmad, N., Rifen, A. A. M., & Wahab, M. H. A. (2016). AES Cardless Automatic Teller Machine (ATM) Biometric security system design using FPGA implementation. In IOP Conference Series: Materials Science and Engineering, 160(1), 012113.

[2]. Betab, G., & Sandhu, R. K. (2014). Fingerprints in Automated Teller Machine-A survey. International Journal of Engineering and Advanced Technology (IJEAT), 3(4), 183-186.

[3]. Boyett, J. H., & Boyett, J. T. (1996). Beyond Workplace 2000: Essential Strategies for the New American Corporation. New York: Dutton.

[4]. Dutta, M., Psyche, K. K., & Yasmin, S. (2017). ATM transaction security using fingerprint recognition. American Journal of Engineering Research, 6(8), 41 -45.

[5]. First bach Nigeria. (2017). Automated Teller Machine (ATM). Retrieved from http://www.firstbanknigeria. com/products/ ebanking/automated-teller-machine- ATM

[6]. Hegde, N., & Sharath, K. R. (2016). Card less ATM Cash Withdrawal: A simple and Alternate Approach. International Journal of Computer Science and Information Technologies, 7(1), 126-128.

[7]. Krishnamurthy, P., & Redddy, M. M. (2012). Implementation of ATM security by using fingerprint recognition and GSM. International Journal of Electronics Communication and Computer Engineering, 3(1), 83-86.

[8]. Mumin, Y. A., Ustarz, Y., & Yakubu, I. (2014). Automated Teller Machine (ATM) operation features and usage in Ghana: Implications for managerial decisions. Journal of Business Administration and Education, 5(2), 137-157.

[9]. Odusina, A. O. (2014). Automated Teller Machine usage And customers' satisfaction. In Nigeria Elite Research Journal of Accounting and Business Management, 2(3), 43-47.

[10]. Pranav. P., & Dawande, T. (2016). ATM transaction with debit card. International Journal of Advanced Resear in Computer and Communication Engineering, 5(7), 258-261.

[11]. Prettam, N., & Gupta, H. (2014). Cardless cash access using biometric ATM security system. International Journal of Enhanced Research in Science Technology and Engineering, 3(11), 13-17.

[12]. Snellman. H. (2006). Automated Teller Machine Network Market Structure and Cash Usage. Scientific Monographs.

[13]. Sunehra, D. (2014). Fingerprint based biometric ATM authentication system. International Journal of Engineering Inventions, 3(11), 22-28.

Full Article (HTML)

Pdf

Research Paper

Infant Cry Recognition System using Autoregressive Model Coefficients

S. R. Fatimah* , A. M. Aibinu**

* Department of Electrical Engineering, Nile University of Nigeria, Abuja, Nigeria.

** Department of Mechatronics Engineering, Federal University of Technology, Minna, Nigeria.

Fatimah, S. R., & Aibinu, A. M. (2018). Infant Cry Recognition System using Autoregressive Model Coefficients. i-manager's Journal on Digital Signal Processing, 6(2), 9-16. https://doi.org/10.26634/jdp.6.2.15591

Abstract

Understanding infants’ needs through crying is a skill acquired by health care givers as well as parents from training and experiences. However, errors may evolve due to variations in judgment and limitations on the human sensory system. Various approaches have been proposed to mimic the classical human based method which also tied results to system dominant errors. This work uses the Autoregressive (AR) model coefficient as features for recognizing infant cry. First, a dataset of infant cry consisting of Hunger, Pain and Normal cry was obtained. Each cry was framed and widowed with overlap to enable the processing of the rapidly changing cry signal. Then AR model coefficients (features) were extracted from the trained Artificial Neural Network (ANN). The extracted features were then used to train an Artificial Neural Network recognition system. The performance of this system was tested using three different activation functions, sampling frequencies and various threshold values. Results show the appropriateness of this new approach.

References

[1]. Abou-Abbas, L., Alaie, H. F., & Tadj, C. (2015). Automatic detection of the expiratory and inspiratory phases in newborn cry signals. Biomedical Signal Processing and Control, 19, 35-43.

[2]. Aibinu, A. M., Salami, M. J. E., & Shafie, A. A. (2010a). Determination of complex-valued parametric model coefficients using Artificial Neural Network technique. advances in artificial neural Systems, 2010, 1687-7594.

[3]. Aibinu, A. M., Salami, M. J. E., & Shafie, A. A. (2010, November). Application of modeling techniques to diabetes diagnosis. In Biomedical Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on (pp. 194-198). IEEE.

[4]. Aibinu, A. M., Salami, M. J. E., & Shafie, A. A. (2011). A novel signal diagnosis technique using pseudo complex- valued autoregressive technique. Expert Systems with Applications, 38(8), 9063-9069.

[5]. Aibinu, A. M., Salami, M. J. E., & Shafie, A. A. (2012). Artificial neural network based autoregressive modeling technique with application in voice activity detection. Engineering Applications of Artificial Intelligence, 25(6), 1265-1276.

[6]. Alaie, H. F., Abou-Abbas, L., & Tadj, C. (2016). Cry-based infant pathology classification using GMMs. Speech Communication, 77, 28-52.

[7]. Baareh, A. K. M., Sheta, A. F., & Khnaifes, K. A. (2006). Forecasting river flow in the USA: A comparison between Auto-Regression and Neural Network non-parametric models, Journal of Computer Science, 2(10), 775-780.

[8]. Bandala, A. A., Lim, A. M., Cai, M. A. D., Bacar, A. J. C., & Manosca, A. C. G. (2014). Modelling and characterization of an Artificial Neural Network for infant cr y recognition using Mel-Frequency Cepstral Coefficients. Proc. of TENCON 2014 – 2014 IEEE Region 10 Conference.

[9]. Bănică, I. A., Cucu, H., Buzo, A., Burileanu, D., & Burileanu, C. (2016, June). Automatic methods for infant cry classification. In Communications (COMM), 2016 International Conference on (pp. 51-54). IEEE.

[10]. Chandralingam, S., Anjaneyulu, T., & Satyanarayana, K. (2012). Estimation of fundamental and formant frequencies of infants cries; a study of infants with congenital heart disorder. Indian Journal of Computer Science and Engineering, 3(4), 574-582.

[11]. Chittora, A., & Patil, H. A. (2015, May). Analysis of normal and pathological infant cries using bispectrum features derived using HOSVD. In BioSignal Analysis, Processing and Systems (ICBAPS), 2015 International Conference on (pp. 151-155). IEEE.

[12]. Designing Buildings Wiki. (2017). Parametric Modeling. Retrieved from https://www.designing buildings.co.uk/wiki/Parametric_modelling

[13]. Ding, J., & Chang, Y. (2011). Noise Reduction in Speech Recognition. Retrieved from https://slideus.org/ philosophy-of-the-money.html?utm_source=noise-reduction- in-speech-recognition

[14]. Garcia, J. O., & Garcia, C. R. (2003, July). Mel-frequency cepstrum coefficients extraction from infant cry for classification of normal and pathological cry with feed-forward neural networks. In Neural Networks, 2003. Proceedings of the International Joint Conference on (Vol. 4, pp. 3140-3145). IEEE.

[15]. Hariharan, M., Chee, L. S., & Yaacob, S. (2012). Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network. Journal of Medical Systems, 36(3), 1309-1315.

[16]. Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial Neural Networks: A tutorial. Computer, 29(3), 31-44.

[17]. Janse, P. V., Magre, S. B., Kurzekar, P. K., & Deshmukh, R. R. (2014). A comparative study between MFCC and DWT Feature Extraction Technique. International Journal of Engineering Research and Technology, 3(1), 3124- 3127.

[18]. Kheddache, Y., & Tadj, C. (2012, July). Newborn's pathological cry identification system. In Information Science, Signal Processing and their Applications (ISSPA), 2012 11^th International Conference on (pp. 1024-1029). IEEE.

[19]. Lederman, D. (2002). Automatic classification of infants' Cry (Master's Thesis, Ben-Gurion University of the Negev).

[20]. Limantoro, W. S., Fatichah, C., & Yuhana, U. L. (2016, October). Application development for recognizing type of infant's cry sound. In Information & Communication Technology and Systems (ICTS), 2016 International Conference on (pp. 157-161). IEEE.

[21]. Naithani, G. (2015). Acoustic Analysis of Infant Cry Signals (Masters Thesis, Tampere University of Technology).

[22]. Onumanyi, A. J., Onwuka, E. N., Aibinu, A. M., Ugweje, O. C., & Salami, M. J. E. (2014). A real valued neural network based autoregressive energy detector for cognitive radio application. International Scholarly Research Notices, Hindawi Publishing Corporation, 2014.

[23]. Orozco, J., & Reyes-Garcia, C. A. (2003, September). Implementation and analysis of training algorithms for the classification of infant cry with feed-forward neural networks. In Intelligent Signal Processing, 2003 IEEE International Symposium on (pp. 271-276). IEEE.

[24]. Patil, H. A. (2009, February). Infant identification from their cry. In Advances in Pattern Recognition, 2009. ICAPR'09. Seventh International Conference on (pp. 107- 110). IEEE.

[25]. Reyes-Galaviz, O. F., & Reyes-Garcia, C. A. (2004). A system for the processing of infant cry to recognize pathologies in recently born babies with neural networks. In 9^thConference Speech and Computer.

[26]. Reyes-Galaviz, O. F., Cano-Ortiz, S. D., & Reyes- García, C. A. (2008, October). Evolutionary-neural system to classify infant cry units for pathologies identification in recently born babies. In Artificial Intelligence, 2008. MICAI'08. Seventh Mexican International Conference on (pp. 330-335). IEEE.

[27]. Rosales-Pérez, A., Reyes-García, C. A., Gonzalez, J. A., Reyes-Galaviz, O. F., Escalante, H. J., & Orlandi, S. (2015). Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model. Biomedical Signal Processing and Control, 17, 38-46.

[28]. Rosita, Y. D., & Junaedi, H. (2016, October). Infant's cry sound classification using Mel-Frequency Cepstrum Coefficients feature extraction and Backpropagation Neural Network. In Science and Technology-Computer (ICST), International Conference on (pp. 160-166). IEEE.

[29]. Sahak, R., Mansor, W., Khuan, L. Y., Zabidi, A., & Yassin, A. I. M. (2012, January). Detection of asphyxia from infant cry using support vector machine and multilayer perceptron integrated with Orthogonal Least Square. In Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on (pp. 906-909). IEEE.

[30]. Saraswathy, J., Hariharan, M., Yaacob, S., & Khairunizam, W. (2012, Februar y). Automatic classification of infant cry: A review. In Biomedical Engineering (ICoBE), 2012 International Conference on (pp. 543-548). IEEE.

[31]. Satapathy, S. C., Bhateja, V., Udgata, S. K., & Pattnaik, P. K. (2013). Proceedings of the 5^th International Conference on Frontiers in Intelligent Computing: Theory and Applications (Vol. 199). Springer Science & Business Media.

[32]. Srijiranon, K., & Eiamkanitchat, N. (2014, October). Application of neuro-fuzzy approaches to recognition and classification of infant cry. In TENCON 2014-2014 IEEE Region 10 Conference (pp. 1-6). IEEE.

[33]. Vocal Tract. (2017). Voice Science Works. Retrieved from https://www.voicescienceworks.org/vocal-tract.html

[34]. Wahid, N. S. A., Saad, P., & Hariharan, M. (2016a). Automatic infant cry pattern classification for a multiclass problem. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(9), 45-52.

[35]. Wahid, N. S. A., Saad, P., & Hariharan, M. (2016b). Automatic infant cry classification using radial basis function network. Journal of Advanced Research in Applied Sciences and Engineering Technology, 4(1), 12-28.

[36]. Zabidi, A., Yassin, I. M., Hassan, H. A., Ismail, N., Hamzah, M. M. A. M., Rizman, Z. I., & Abidin, H. Z. (2017). Detection of asphyxia in infants using deep learning Convolutional Neural Network (CNN) trained on Mel Frequency Cepstrum Coefficient (MFCC) features extracted from cry sounds. Journal of Fundamental and Applied Sciences, 9(3S), 768-778.

Full Article (HTML)

Pdf

Research Paper

Adaptive Traffic Control System using Modified Round Robin and Genetic Algorithm

Nasir Mohammed Sadiq* , Oluwaseun Adeniyi Ojerinde, Solomon A. Adepoju*

*-*** Department of Computer Science, Federal University of Technology, Minna, Nigeria.

Sadiq, N. M., Ojerinde, O. A., & Adepoju, S. A. (2018). Adaptive Traffic Control System using Modified Round Robin and Genetic Algorithm. i-manager's Journal on Digital Signal Processing, 6(2), 17-23. https://doi.org/10.26634/jdp.6.2.15592

Abstract

Adaptive Traffic Control System (ATCS) serves as a main element in the constituents with which traffic control flow is achieved in fast developing, and developed urban areas. ATCS, however causes more delays on vehicles due to the fact that it is made up of intersecting points. Ensuring maximum efficiency at intersections has remained a challenge due to its dynamic nature of traffic. Additionally, a number of different methods that can be used to achieve higher performance at road traffic intersections have been recently proposed to engineers. In this study, a new and different method based on modified round robin scheduling algorithm through genetic algorithm technique to optimize the performance (in terms of timing) of a signalized intersection in one of the busiest and most crowded roads of Minna, Niger State – Nigeria (at Obasanjo shopping complex area). The technique uses an initial timing pattern to generate newer offspring (in terms of delay duration) to analyze cost function and to check if a global optimum is reached. This technique outweighs current techniques because the data upon which the nature of the system is built is relatively more phenomenal, as it puts into consideration the exact nature of the lane in many possible occurrences. In this work, a global optimum was reached at only a few number of iteration on the whole Genetic Algorithm process.

References

[1]. Nipa, L. N., & Islam, M. (2015). Intelligent Traffic Control System based on Round. Int. J. Sci. Res. Publ., 5(8),1-8.

[2]. Carr, J. (2015). An Introduction to Genetic Algorithms. senior project, 1-40.

[3]. Gündoğan, F., Karagoz, Z., Kocyigit, N., Karadag, A., Ceylan, H., & Murat, Y. Ş. (2014). An evaluation of Adaptive Traffic Control System in Istanbul. Turkey. J. Traffic Logist. Eng., 2(3), pp-198-201.

[4]. Hasan, M., Saha, G., Hoque, A., & Majumder, B. (2014). Smart Traffic Control System with application of Image Processing Techniques. 3^rd Int. Conf. Informatics, Electron. Vis. (pp. 1-4).

[5]. Kabir, A. N., & Salam, K. M. A. (2016). Implementation of an Intelligent Traffic Control System: The use of FPGA and Verilog HDL. J. Mod. Sci. Technol., 4(1), 154-162.

[6]. Mahajan, S., Atiwadkar, A., Patil, K., Lande, T., & Choudhari, P. S. (2016). Universal Network for Intelligent Traffic Control System: Stolen vehicle detection, Emergency vehicle clearance, Fine Collection and Dynamic Traffic Light Control. Int. Res. J. Eng. Technol., 3(6), 543-547.

[7]. Mishra, A., & Singh, K. (2015). Density based Intelligent Traffic Control System using IR Sensors. Int. J. Sci. Res., 4(5), 2277-2278.

[8]. Ou, H., & Wang, Y. (2016). Development of Intelligent Traffic Control System based on Internet of Things and FPGA Technology in PROTEUS, Int. Conf. Educ. Manag. Comput. Soc. (EMCS) (pp. 405-409).

[9]. Pandit, V., Doshi, J., Mehta, D., Mhatre, A., & Janardhan, A. (2014). Smart traffic control system using image processing. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS), 3(1), 280-283.

[10]. Vahedha, & Jyothi, B. N. (2017). Smart traffic control system using ATMEGA328 micro controller and arduino software. Int. Conf. Signal Process. Commun. Power Embed. Syst. SCOPES 2016 - Proc. (pp. 1584-1587).

Full Article (HTML)

Pdf

Research Paper

Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries

E. M. Dogo* , N. I. Nwulu, O. M. Olaniyi, C. O. Aigbavboa, T. Nkonyana

,, Department of Electrical and Electronics Engineering Science, University of Johannesburg, South Africa.

* Department of Computer Engineering, Federal University of Technology Minna, Nigeria.

**** Department of Construction Management and Quantity Survey, University of Johannesburg, South Africa.

Dogo, E. M., Nwulu, N. I., Olaniyi, O. M., Aigbavboa, C. O., Nkonyana, T. (2018). Blockchain 3.0: Towards A Secure Ballotcoin Democracy Through A Digitized Public Ledger in Developing Countries. i-manager's Journal on Digital Signal Processing, 6(2), 24-35. https://doi.org/10.26634/jdp.6.2.15593

Abstract

This paper reviews scholarly articles on the application of blockchain technology for secure electronic voting (e-voting). Furthermore, the feasibility of using blockchain technology to replace the existing manual or semidigitized voting system in developing countries with Nigeria as a case study is performed. To analyse the current state and preparedness of adopting Blockchain Enabled E-voting (BEEV) system in Nigeria, this paper employs the qualitative SWOT (Strengths, Weaknesses, Opportunities and Threats) and PEST (Political, Economic, Social and Technological) analysis approach. This evaluation leads us to identify internal and external factors and the strategic direction in adopting BEEV in Nigeria. It is the authors’ opinion that this approach could also be tailored to evaluate situations of other developing countries.

References

[1]. Ahmad, S., Abdullah, S. A. J., & Arshad, R. B. (2015). Participation and voting policy process in Nigeria: A qualitative study. Mediterranean Journal of Social Sciences, 6(4), 362-374.

[2]. Al-Saqaf, W., & Seidler, N. (2017). Blockchain technology for social impact: Opportunities and challenges ahead. Journal of Cyber Policy, 2(3), 338-354.

[3]. Ayed, A. B. (2017). A conceptual secure Blockchain-based electronic voting system. International Journal of Network Security & Its Applications, 9(3), 1-9.

[4]. Bahga, A., & Madisetti, V. K. (2016). Blockchain platform for industrial internet of things. Journal of Software Engineering and Applications, 9(10), 533-546.

[5]. Baliga, A. (2017). Understanding blockchain consensus models. Persistent. Retrieved from https://pdfs.semanticscholar.org/da8a/37b10bc1521a4 d3de925d7ebc4 4bb606d740.pdf 2017, on 20 March 2018

[6]. Bistarelli, S., Mantilacci, M., Santancini, P., & Santini, F. (2017, April). An end-to-end voting-system based on bitcoin. In Proceedings of the Symposium on Applied Computing (pp. 1836-1841). ACM.

[7]. Biswas, K., & Muthukkumarasamy, V. (2016, December). Securing smart cities using blockchain technology. In High Performance Computing and Communications; IEEE 14^th International Conference on Smart City; IEEE 2^nd International Conference on Data Science and Systems (HPCC / Smart City / DSS), 2016 IEEE 18^th International Conference on (pp. 1392-1393). IEEE.

[8]. Bitcoin Africa. (2017). African's Blockchain Startups. Retrieved from https://bitcoinafrica.io/category/meet- africas- blockchain-startups/, on 10 April 2018

[9]. Castro, M., & Liskov, B. (1999, February). Practical Byzantine fault tolerance. In OSDI, 99, 173-186.

[10]. Chalaemwongwan, N., & Kurutach, W. (2018, January). State of the art and challenges facing consensus protocols on blockchain. In Information Networking (ICOIN), 2018 International Conference on (pp. 957-962). IEEE.

[11]. Charities Aid Foundation. (2015a). Giving Unchained: Philanthropy and the Blockchain, Giving Thought Discussion paper no.4. Retrieved from https://www.cafonline.org/ docs/default-source/about-us-publications/givingunchained-philanthropy-and-theblockchain. pdf?sfvrsn=4, on 10 April 2018

[12]. Charities Aid Foundation. (2015b). Blockchain Technology Could Revolutionise Charitable Giving - Report. Retrieved from https://www.cafonline.org/ about-us/media-office/media-office-archive- article/ blockchain-technology-could-revolutionise-charitable- giving+&cd=1&hl=en&ct=clnk&gl=in

[13]. Chiang, L. (2009). Trust and security in the e-voting system. Electronic Government, an International Journal, 6(4), 343-360.

[14]. Deloitte. (2017). Blockchain Technology and its Potential in Taxes. Retrieved from https://www2.deloitte. com/content/dam/Deloitte/pl/Documents/Reports/ pl_Bl ockchain-technology-and-its- potential-in-taxes-2017- EN.PDF

[15]. Dill, D. L., & Rubin, A. D. (2004). E-voting Security. In IEEE Security & Privacy, 2(1), 22-23.

[16]. Dontsov, D. (2018). How blockchain technology can drive the legal industry forward. Law Journal Newsletters, Retrieved from http://www.lawjournalnewsletters.com/ sites/lawjournalnewsletters/2018/01/01/how-blockchain- technology- can-drive-the-legal-industr y-forward/?sl return=20180614094636

[17]. Ha, H., & Coghill, K. (2006). E-Government in Singapore-A SWOT and PEST Analysis. Asia-Pacific Social Science Review, 6(2), 103-130.

[18]. Hegadekatti, K. (2017). Analysis of present day election processes vis-à-vis elections through Blockchain technology. Blockchain Elections, 1-13.

[19]. Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34.

[20]. Ibrahim, S., Kamat, M., Salleh, M., & Aziz, S. R. A. (2003, January). Secure E-voting with blind signature. In Telecommunication Technology, 2003. NCTT 2003 Proceedings. 4^th National Conference on (pp. 193-197). IEEE.

[21]. Jan, J. K., Chen, Y. Y., & Lin, Y. (2001, October). The design of protocol for e-voting on the Internet. In Security Technology, 2001 IEEE 35^th International Carnahan Conference on (pp. 180-189). IEEE.

[22]. Kimbel, K. (2018). The Secret behind the Blockchain Technology. Retrieved from https://pecb.com/past- webinars/ the-secret-behind-the-blockchain-technology

[23]. Kubjas, I. (2017). Using blockchain for enabling internet voting. Retrieved from https://pdfs.semantic scholar.org/8d92/1dbfe6bebefa2599ca6afc7eeae 82210a71d.pdf

[24]. Kuo, T. T., Kim, H. E., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and health care applications. Journal of the American Medical Informatics Association, 24(6), 1211- 1220. https://doi.org/10.1093/ jamia/ocx068

[25]. Lan, G., Brewster, C., Spek, J., Smeenk, A., & Top, J. (2017). Blockchain for Agriculture and Food; Findings from the pilot study. Wageningen Economic Research, Report 2017-112.

[26]. Lielacher, A. (2018). Sierra Leone successfully holds World's First Blockchain-Enabled election. Bitcoin Africa. Retrieved from https://bitcoinafrica.io/2018/03/09/sierra- leone- blockchainelection/, on 10 April 2018

[27]. Lin, I. C., & Liao, T. C. (2017). A survey of Blockchain security issues and challenges. IJ Network Security, 19(5), 653-659.

[28]. Liu, Y., & Wang, Q. (2017). An e-voting protocol based on blockchain. IACR Cryptol. ePrint Arch., Santa Barbara, CA, USA, Tech. Rep, 2017, 1043.

[29]. Marwala, T., & Xing, B. (2018). Blockchain and Artificial Intelligence. University of Johannesburg, Auckland Park, Republic of South Africa. Retrieved from https://arxiv.org/ftp/arxiv/papers/1802/1802.04451.pdf, on 20 April 2018.

[30]. Meter, C. (2015). Design of Distributed Voting Systems. Heinrich-Heine-Universität Düsseldorf. Retrieved from https://arxiv.org/pdf/1702.02566.pdf

[31]. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system, pp. 1-9. Retrieved from http://bitcoin.org/bitcoin.pdf

[32]. O'Dair, M., Beaven, Z., Neilson, D., Osborne, R., & Pacifico, P. (2016). Music on the blockchain. Middlesex University. Retrieved from https://www.mdx.ac.uk/data/ assets/pdf_file/ 0026/230696/Music-On-The-Blockchain. pdf

[33]. Oke, B. A., Olaniyi, O. M., Aboaba, A. A., & Arulogun, O. T. (2017, October). Developing multifactor authentication technique for secure electronic voting system. In Computing Networking and Informatics (ICCNI), 2017 International Conference on (pp. 1-6). IEEE.

[34]. Olaniyi, O. M., Arulogun, O. T., & Omidiora, E. O. (2012). Towards an Improved Stegano-Cryptographic Model for Secured Electronic Voting. African Journal of Computing and ICT (Journal of IEEE Nigeria Computer Section), 5(6), 10-16.

[35]. Olaniyi, O. M., Arulogun, O. T., Omidiora, E. O., & Okediran, O. O. (2015). Enhanced steganocryptographic model for secure electronic voting. Journal of Information Engineering and Applications, 5(4), 1-15.

[36]. Ølnes, S., Ubacht, J., & Janssen, M. (2017). Blockchain in government: Benefits and implications of distributed ledger technology for information sharing. Government Information Quarterly, 34(3), 355-364.

[37]. Osho, O., Yisa, V. L., & Jebutu, O. J. (2015, November). E-voting in Nigeria: A survey of voters' perception of security and other trust factors. In Cyberspace (CYBER-Abuja), 2015 International Conference on (pp. 202-211). IEEE.

[38]. Park, L. W., Lee, S., & Chang, H. (2018). A sustainable home energy prosumer-chain methodology with energy tags over the Blockchain. Sustainability, 10(3), 658.

[39]. Policy and Legal Advocacy Centre (PLAC). (2017). Report of the Ad-hoc Committee on the Legislative Agenda. Retrieved from http://placng.org/wp/wpc ontent/uploads/2016/06/8TH-SENATE-DRAFTLEGISLATIVE- AGENDA-2.7 15.pdf, on 15 April 2018.

[40]. Pop, C., Cioara, T., Antal, M., Anghel, I., Salomie, I., & Bertoncini, M. (2018). Blockchain based decentralized management of demand response programs in smart energy grids. Sensors, 18(1), 162.

[41]. Rabah, K. (2017). Overview of Blockchain as the Engine of the 4^th Industrial Revolution. Mara Research Journal of Business & Management, 1(1), 125-135.

[42]. Rooksby, J., & Dimitrov, K. (2017). Trustless education? A blockchain system for university grades. In New Value Transactions: Understanding and Designing for Distributed Autonomous Organisations, Workshop at DIS. Retrieved from http://johnrooksby.org/papers/ DAOworkshop_rooksby.pdf, on 20 May 2017

[43]. Rosa, J. L.D. L, Torres-Padrosa, V., El-Fakdi, A., Gibovic, D., Hornyák, O., Maicher, L., & Miralles, F. (2017). A survey of blockchain technologies for open innovation. In 4^th Annual World Open Innovation Conf. WOIC (pp. 14- 15).

[44]. Shamos, M., & Yasinsac, A. (2012). Realities of Evoting security. IEEE Security & Privacy, 10(5), 16-17.

[45]. Sikorski, J. J., Haughton, J., & Kraft, M. (2017). Blockchain technology in the chemical industry: Machine-to-machine electricity market. Applied Energy, 195, 234-246.

[46]. UN Economic Commission for Africa. (2017). Blockchain Technology in Africa. United Nations Expert Group Meeting in Addis Ababa. Retrieved from https://www.uneca.org/sites/default/files/images/e17012 84_concept_ note_egm_22_november_2017.pdf, on 10 April 2018

[47]. Vanguard Nigeria. (2015). After Initial Card Reader Failure - Nigerians Persevere, Vote in Peaceful Elections. Retrieved from https://www.vanguardngr.com/2015/03/ after-initial-card-reader- failure-nigerians-perseverevote- in-peaceful-elections/, on Mar 30, 2019.

[48]. Verified Voting Foundation. (2017). Senate Amends Electoral Act, Approves Electronic Voting. Retrieved from https://thevotingnews.com/senate-amends-electoralact- approves-electronic-voting-ynaija/ on 12 April 2018.

[49]. Vukolić, M. (2015, October). The quest for scalable blockchain fabric: Proof-of-work vs. BFT replication. In International Workshop on Open Problems in Network Security (pp. 112-125). Springer, Cham.

[50]. Wu, Y. (2017). An e-voting system based on blockchain and ring signature (Master's Thesis, University of Birmingham). Retrieved from https://www.dgalindo.es/ mscprojects/yifan.pdf

Full Article (HTML)

Pdf

Research Paper

Privacy Preserving Classification over Encrypted Data Using Fully Homomorphic Encryption Technique

Abdullahi Monday Jubrin* , Victor Onomza Waziri, Muhammad Bashir Abdullahi*, Idris Ismaila****

, Department of Computer Science, Federal University of Technology, Minna, Nigeria, and Department of Computer Science, Veritas University, Abuja, Nigeria.

,**** Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.

Jubrin, A. M., Abdullahi, M. B., Waziri, V. O., Ismaila, I. (2018). Privacy Preserving Classification Over Encrypted Data using Fully Homomorphic Encryption Technique. i-manager's Journal on Digital Signal Processing, 6(2), 36-47. https://doi.org/10.26634/jdp.6.2.15590

Abstract

Applying Machine Learning to a problem which involves medical, financial, or other types of sensitive data needs careful attention in order to maintaining data privacy and security. This paper presents a model for privacy preserving classification and demonstrated that, by using a decision tree classifier, it is possible to perform a privacy preserving classification operation on an encrypted data residing on an untrusted server using the technique of Fully Homomorphic Encryption. First, the paper presented a model for the design and implementation of privacy preserving decision tree classifier over encrypted data. Also, Fully Homomorphic Encryption technique was used to secretly carry out classification on ciphertext using decision tree model built out of confidential medical data. The classifier was implemented using the SEAL homomorphic library and evaluation was done using encrypted medical datasets. The experimental results demonstrated high accuracy of the ciphertext classifier (when compared to the plaintext data equivalent) and efficiency (compared to other classifier on similar tasks). It takes less than 5 seconds (depending on the depth) to perform classification over an encrypted hepatitis feature vector dataset.

References

[1]. Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2017). A survey on homomorphic encryption schemes: Theory and Implementation. arXiv preprint arXiv:1704.03578.

[2]. AdaPopa, R. (2014). Building practical systems that compute on encrypted data (Doctoral Dissertation, Massachusetts Institute of Technology).

[3]. Albrecht, M. R., Player, R., & Scott, S. (2015). On the concrete hardness of Learning with Errors. J. Math. Cryptol., 9(3), 169-203.

[4]. Ames, S., Venkitasubramaniam, M., Page, A., Kocabas, O., & Soyata, T. (2015). Secure health monitoring in the cloud using homomorphic encryption: A branching-program formulation. In Enabling Real-Time Mobile Cloud Computing through Emerging Technologies (pp. 116-152). IGI Global.

[5]. Barni, M., Failla, P., Kolesnikov, V., Lazzeretti, R., Sadeghi, A. R., & Schneider, T. (2009, September). Secure evaluation of private linear branching programs with medical applications. In European Symposium on Research in Computer Security (pp. 424-439). Springer, Berlin, Heidelberg.

[6]. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

[7]. Bonnoron, G., & Fontaine, C. (2006). A note on Ring- LWE security in the case of Fully Homomorphic Encryption. In: Patra A., Smart N. (Eds) Progress in Cryptology – INDOCRYPT 2017. INDOCRYPT 2017. Lecture Notes in Computer Science (Vol 10698). Springer, Cham.

[8]. Bos, J. W., Lauter, K., & Naehrig, M. (2014). Private predictive analysis on encrypted medical data. J. Biomed. Inform., 50, 234-243.

[9]. Bost, R., Popa, R., Tu, S., & Goldwasser, S. (2015). Machine Learning Classification over Encrypted Data. Ndss '15 (pp.1-31).

[10]. Brakerski, Z. (2012). Fully homomorphic encryption without modulus switching from classical Gap SVP. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 7417, 868-886.

[11]. Brakerski, Z., & Vaikuntanathan, V. (2011). Fully homomorphic encryption from Ring-LWE and security for key dependent messages. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 6841, 505-524.

[12]. Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2012). (Leveled) Fully Homomorphic Encryption without Bootstrapping. ACM Trans. Comput. Theory, 6(3), 1-36.

[13]. Carpov, S., Sirdey, R., Costantino, G., & Martinelli, F. (2017). Practical Privacy Preserving Medical Diagnosis t h using Homomorphic Encr yption. 2016 IEEE 9^thInternational Conference on Cloud Computing (CLOUD) (pp. 593-599).

[14]. Chen, H., Laine, K., & Player, R. (2013). Simple Encrypted Arithmetic Library - SEAL V 2.1. In: Brenner M. et al. (Eds) Financial Cryptography and Data Security. FC 2017. Lecture Notes in Computer Science (Vol 10323). Springer, Cham.

[15]. Dijk, M. V., Gentry, C., Halevi, S., & Vaikuntanathan, V. (2010). Fully homomorphic encryption over the integers. Adv. Cryptology- EUROCRYPT '10 ( pp. 24-43).

[16]. Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig, M., & Wernsing, J. (2016). CryptoNets: Applying neural networks to Encrypted data with high throughput and accuracy - Microsoft research. Microsoft Res. TechReport, pp. 1-12.

[17]. Fan, J., & Vercauteren, F. (2012). Somewhat Practical Fully Homomorphic Encryption. Proc. 15^th Int. Conf. Pract. Theory Public Key Cryptogr. (pp.1-16).

[18]. Gentry, C. (2009). Fully homomorphic encryption st using ideal lattices. Proc. 41^stAnnu. ACM Symp. Symp. theory Comput. - STOC '09. (p. 169).

[19]. Graepel, T., Lauter, K., & Naehrig, M. (2013). ML confidential: Machine learning on encrypted data. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 7839, 1-21.

[20]. Halevi, S., & Shoup, V. (2015). HElib_HElib Documentation.

[21]. HIPAA, (2014). Health Information Privacy_ HHS. U.S. Department of Health & Human Services.

[22]. Khedr, A., Gulak, G., & Vaikuntanathan, V. (2016). SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers. IEEE Trans. Comput., 65(9), 2848-2858.

[23]. Kocabaş, Ö. (2016). Design and Analysis of Privacypreserving Medical Cloud Computing Systems (Doctoral Dissertation, University of Rochester).

[24]. Kocabas, O., Soyata, T., & Aktas, M. K. (2016). Emerging Security Mechanisms for Medical Cyber Physical System. IEEE/ACM Trans. Comput. Biol. Bioinforma., 13(3), 401-416.

[25]. Kumar, V. (2015). Data Mining and Knowledge Discovery Series. Chapman & Hall/CRC Press.

[26]. Lindner, R., & Peikert, C. (2011). Better key sizes (and Attacks) for LWE- based encryption, In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6558, 319-339.

[27]. Lyubashevsky, Peikert, C., & Regev, O. (2010). On ideal lattices and learning with errors over rings. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 6110(15848), 1-23.

[28]. Regev, O. (2005). On lattices, learning with errors, random linear codes, and cryptography. J. ACM, 56(6), 1- 40.

[29]. Schneider, T. (2009). Secure evaluation of private linear branching programs with medical applications. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 5789, 424-439.

[30]. Shouval, R., Bondi, O., Mishan, H., Shimoni, A., Unger, R., & Nagler, A. (2014). Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT. Bone Marrow Transplant, 49(3), 332-337.

[31]. Wu, D. J., Feng, T., Naehrig, M., & Lauter, K. (2016). Privately Evaluating Decision Trees and Random Forests. Proceedings on Privacy Enhancing Technologies, 2016(4), 335-355.

i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 6 Issue 2 April - June 2018

A Simulation Model for Cardless Automated Teller Machine Transactions

O. S. Adewale* , J. O. Mebawondu**, O. J. Mebawondu***, M. N. Suleiman****

Adewale, O. S., Mebawondu, J. O., Suleimano, M. N., & Mebawondu, J. (2018). A Simulation Model For Cardless Automated Teller Machine Transactions. i-manager's Journal on Digital Signal Processing, 6(2), 1-8. https://doi.org/10.26634/jdp.6.2.15588

Abstract

References

Full Article (HTML)

Pdf

Infant Cry Recognition System using Autoregressive Model Coefficients

S. R. Fatimah* , A. M. Aibinu**

* Department of Electrical Engineering, Nile University of Nigeria, Abuja, Nigeria. ** Department of Mechatronics Engineering, Federal University of Technology, Minna, Nigeria.

Fatimah, S. R., & Aibinu, A. M. (2018). Infant Cry Recognition System using Autoregressive Model Coefficients. i-manager's Journal on Digital Signal Processing, 6(2), 9-16. https://doi.org/10.26634/jdp.6.2.15591

Abstract

References

Full Article (HTML)

Pdf

Adaptive Traffic Control System using Modified Round Robin and Genetic Algorithm

Nasir Mohammed Sadiq* , Oluwaseun Adeniyi Ojerinde**, Solomon A. Adepoju***

*-*** Department of Computer Science, Federal University of Technology, Minna, Nigeria.

Sadiq, N. M., Ojerinde, O. A., & Adepoju, S. A. (2018). Adaptive Traffic Control System using Modified Round Robin and Genetic Algorithm. i-manager's Journal on Digital Signal Processing, 6(2), 17-23. https://doi.org/10.26634/jdp.6.2.15592

Abstract

References

Full Article (HTML)

Pdf

Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries

E. M. Dogo* , N. I. Nwulu**, O. M. Olaniyi***, C. O. Aigbavboa****, T. Nkonyana*****

Dogo, E. M., Nwulu, N. I., Olaniyi, O. M., Aigbavboa, C. O., Nkonyana, T. (2018). Blockchain 3.0: Towards A Secure Ballotcoin Democracy Through A Digitized Public Ledger in Developing Countries. i-manager's Journal on Digital Signal Processing, 6(2), 24-35. https://doi.org/10.26634/jdp.6.2.15593

Abstract

References

Full Article (HTML)

Pdf

Privacy Preserving Classification over Encrypted Data Using Fully Homomorphic Encryption Technique

Abdullahi Monday Jubrin* , Victor Onomza Waziri**, Muhammad Bashir Abdullahi***, Idris Ismaila****

*,*** Department of Computer Science, Federal University of Technology, Minna, Nigeria, and Department of Computer Science, Veritas University, Abuja, Nigeria. **,**** Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.

Jubrin, A. M., Abdullahi, M. B., Waziri, V. O., Ismaila, I. (2018). Privacy Preserving Classification Over Encrypted Data using Fully Homomorphic Encryption Technique. i-manager's Journal on Digital Signal Processing, 6(2), 36-47. https://doi.org/10.26634/jdp.6.2.15590

Abstract

References

Full Article (HTML)

Pdf

O. S. Adewale* , J. O. Mebawondu, O. J. Mebawondu*, M. N. Suleiman****

* Department of Electrical Engineering, Nile University of Nigeria, Abuja, Nigeria.

** Department of Mechatronics Engineering, Federal University of Technology, Minna, Nigeria.

Nasir Mohammed Sadiq* , Oluwaseun Adeniyi Ojerinde, Solomon A. Adepoju*

E. M. Dogo* , N. I. Nwulu, O. M. Olaniyi, C. O. Aigbavboa, T. Nkonyana

Abdullahi Monday Jubrin* , Victor Onomza Waziri, Muhammad Bashir Abdullahi*, Idris Ismaila****

, Department of Computer Science, Federal University of Technology, Minna, Nigeria, and Department of Computer Science, Veritas University, Abuja, Nigeria.

,**** Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.