Qualification: 
Ph.D, M.Tech, BE
r_prasannakumar@blr.amrita.edu

Dr. R. Prasanna Kumar serves as a Assistant Professor - Selection Grade in the Department of Computer Science and Engineeing, School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru. He has 18 years of experience in teaching. His areas of interest includes Data Analytics, Machine Learning, Theory of Computation, Compiler Design and Python Programming.

Dr. R. Prasanna Kumar has guided 7 M.E projects and is Doctoral Committee Member for 2 Ph.D. Research Scholars in Anna University, Chennai. He has delivered many guest Lectures and acted as resource person in Faculty development programs. He has also acted as program committee member and session chair in national and international conferences.

Education

  • Ph. D.
    Anna University, Chennai
  • M. Tech. in Computer Science and Engineering
    Dr. MGR Educational and Research Institute, Chennai.
  • B. E. in Computer Science and Engineering
    Sri Venkateswara College of Engineering Sriperumbudur, University of Madras, Chennai

Certifications

  • Cisco Networking Academy Certification on “Introduction to Cyber Security course".
  • ORACLE academy certification on “Database Programming with PL/SQL”.
  • Cisco Networking Academy Certification on MODULE – I in CCNA.

Publications

Publication Type: Journal Article

Year of Publication Title

2017

R. Prasanna Kumar, “A Survey on Real-Time Automated Gridlock Control System”, International Journal of Innovative Research in Engineering & Management (IJIREM) , vol. 4, no. 1, 2017.

2017

R. Prasanna Kumar, “Hazardous Gas Detection and Alerting Using Sensor ”, International Journal of Innovative Research in Engineering & Management (IJIREM) , vol. 4, no. 1, 2017.

2015

T. Ravi and R. Prasanna Kumar, “Data Perturbation Techniques for Privacy Preservation in Association Rule Mining”, Australian Journal of Basic and Applied Sciences, vol. 9, no. 20, pp. 220-227, 2015.[Abstract]


In recent, data mining is becoming a popular analysis tool to extract knowledge from collection of large amount of data. The protection of the confidentiality of sensitive information in a database becomes a critical issue when releasing data to outside parties. Association analysis is a powerful and popular tool for discovering relationships hidden in large data sets. These process increases the legal responsibility of the parties. So, it is severe to reliably protect their data due to legal and customer concerns. In this paper, a review of the state-of-the-art methods of data perturbation techniques for privacy preservation is presented.

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2014

T. Ravi, R. Prasanna Kumar, and Napa, K. Kumar, “A non synthetic data perturbation technique for privacy preservation in association rule mining”, International Journal of Applied Engineering Research, vol. 9, no. 24, pp. 24311-24320, 2014.[Abstract]


For specific business problems, organizations share data and outsource. Preserving privacy of private data holds a vital role in business analytics. Consulting firms often handle sensitive third party data as part of client projects. By sharing their data, organizations face great risks while most of this sharing takes place with little furtiveness. These process increases the legal responsibility of the parties. So, it is severe to reliably protect their data due to legal and customer concerns. In this paper, a review of the state-of-theart methods for privacy preservation is presented. A novel perturbation technique using non synthetic additive perturbation technique for association rule mining is proposed in this paper. The above technique minimizes information loss that is common in synthetic perturbed data.

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2011

R. Prasanna Kumar, “Quality of Service Based Trust Management System A Review”, International Journal of Engineering Science and Technology , vol. 3, 2011.

2010

R. Prasanna Kumar, “Record Matching over Query Results using Fuzzy Ontological Document Clustering”, 2010.