Call for Paper

CAE solicits original research papers for the July 2021 Edition. Last date of manuscript submission is June 30, 2021.

Read More

Applying Local Optimization Algorithms in Clustering Combination with Diversity Maximization

Z. Faizal Khan, Mohammed Khaleel Anwar. Published in Algorithms.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Z. Faizal Khan, Mohammed Khaleel Anwar
10.5120/cae2016652160

Faizal Z Khan and Mohammed Khaleel Anwar. Article: Applying Local Optimization Algorithms in Clustering Combination with Diversity Maximization. Communications on Applied Electronics 4(8):50-54, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Z. Faizal Khan and Mohammed Khaleel Anwar},
	title = {Article: Applying Local Optimization Algorithms in Clustering Combination with Diversity Maximization},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {8},
	pages = {50-54},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Automated searching needs is an effective key for the development of cryptography algorithms. The retrieval of encrypted contents from a huge set of distributed databases leads to complexity in the grid computing environment. This paper is based on a search of some set of keywords in grid computing environment. Most of the key based cryptography algorithms developed by earlier researchers are more effective but each and every algorithm has its own disadvantages. Majorly, there arises a huge need to direct the data contents to Grid computing system in the form of cryptic. No algorithm in an intelligible advanced form has been available to the researchers. Complexity and computational complexity present in these algorithms make the retrieval task more inefficient. In order to overcome these drawbacks, we proposed a novel fuzzy based optimization technique in the Grid computing environment based on an encrypted text along with Fuzzy rules. We obtained a satisfactory result in keyword search using this novel method. The result obtained was most effective and the retrieval time is very much reduced in the Grid computing environment.

References

  1. Ayad Ibrahim, Hai Jin, Ali A. Yassin, Deqing Zou , “Approximate Keyword-based Search over Encrypted Cloud Data”, IEEE International Conference on e-Business Engineering, pp 238 -245,2012.
  2. Boldyreva.A, Chenette.N, Lee.Y, and O’Neill.A.,”Order- Preserving Symmetric Encryption “, Proc. International Conf. Advances in Cryptology (Eurocrypt ’09), 2009.
  3. Boneh.D, Crescenzo.G.d, Ostrovsky.R, and Persiano.G.,”Public Key Encryption with Keyword Search,” Proc. International Conf. Advances in Cryptology, 2004.
  4. Faizal Khan, Z & Kannan, “Intelligent Approach for Segmenting CT Lung Images Using Fuzzy Logic with Bitplane”, Journal of Electrical Engineering and Technology, Vol. 9, No. 4, pp-742- 752, 2014
  5. Chang.Y.C and Mitzenmacher.M, “Privacy Preserving Keyword Searches on Remote Encrypted Data “, Proc. International Conf. Applied Cryptography and Network Security, 2005.
  6. Changjiang Hou, Fei Liu , Hongtao Bai , Lanfang Ren ,“Public-Key Encryption with Keyword Search from Lattice”, Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp 336-339,2013.
  7. Cong Wang, Kui Ren, “Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data “, IEEE transactions on parallel and distributed systems, Vol. 23, No. 8, pp 1467-1479 August 2012.
  8. Dr. Z. Faizal Khan, Dr. S. Veeramalai and J. Velmurugan, “Automatic Keyword Search using Encrypted Text in Grid Computing Architecture”, Journal of Applied Sciences Research, Vol 11, issue 14, pp- 190-194, September 2015.
  9. Goh.E.J , Secure Indexes,” Technical Report 2003/216, Cryptology ePrint Archive, http://eprint.iacr.org/, 2003.
  10. Goldreich.O and Ostrovsky.R, “Software Protection and Simulation on Oblivious Rams,” Journal ACM, Volume 43, No. 3, pp. 431-473, 1996.
  11. Jayalatchumy.D , Kadhirvelu.D, Ramkumar.P , “Design of an Intelligent Answering System Through Agent Based Search Engine Using Grid Technology” First International Conference on Emerging Trends in Engineering and Technology, pp 519-524,2008.
  12. Kyriaki Z.Gkoutioudi, Helen D.Karatza, “Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm”, Journal of Grid Computing, Volume 10, No 2, pp 311-323, June 2012 .
  13. Kiyohide Nakauchi, Yuichi Ishikawa, Hiroyuki Morikawa, Tomonori Aoyama,: Peer-to-Peer Keyword Search Using Keyword Relationship, 3rd IEEE International Symposium on Cluster Computing and the Grid, pp 359-366, May 2003.
  14. Lee.Y.H, Leu.S, and R.-S. Chang, “Improving Job Scheduling Algorithms in a Grid Environment,” Future Generation Computer Systems, volume 27, pp. 991-998, 2011.
  15. Marios D. Dikaiakos, Asterios Katsifodimos, George Pallis. Miner soft: Software Retrieval in Grid and Cloud Computing Infrastructures." ACM Transactions on Internet Technologies. Volume 12, No 1, June 2012.
  16. Simone A.Ludwig, Azin Moallem,”Swarm Intelligence Approaches for Grid Load Balancing”, Journal of Grid Computing, Volume 9, No3, pp 279-301, September 2011.
  17. Singhal.A, Modern Information Retrieval: A Brief Overview, IEEE Data Engineering, Volume 24, No 4, pp 35-43, 2011.
  18. Zerr.S, Olmedilla.D, Nejdl.W, and Siberski.W, Zerberr, “Top-k Retrieval from a Confidential Index”, Proceedings International Conference Extending Database Technology: Advances in Database Technology, 2009.

Keywords

Cryptography algorithms, computational complexity, scheduling, Grid Computing Environment, Fuzzy logic, peer to peer search system.