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Journal of Discrete Mathematical Sciences and Cryptography Volume 24, 2021 - Issue 8: Cryptography and Security Challenges in Artificial Intelligence & IoT Applications
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Research Article

Application of cryptography in data privacy using fuzzy graph theory

Anu Pius1 Department of Mathematics, PRIST (Deemed to be University), Thanjavur613403, Tamil Nadu, IndiaCorrespondence[email protected]
&
D. R. Kirubaharan2 Department of Mathematics, PRIST (Deemed to be University), Thanjavur613403, Tamil Nadu, India E-mail: [email protected]
Pages 2389-2401 | Published online: 27 Dec 2021

References

  • W. Zhang, “From Equilibrium-Based Business Intelligence to Information Conservational Quantum-Fuzzy Cryptography A Cellular Transformationof Bipolar Fuzzy Setsto Quantum Intelligence Machinery, April 2018, doi: https://doi.org/10.1109/TFUZZ.2017.2687408.
  • N. Khokhlov, S. Kanavin and A. Rybokitov, “Modeling Information Security Infringements in Mobile Self Organizing Network of Communication Using Fuzzy Logic and Theory of Graphs,” 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA), 2019, pp. 60-63, doi: https://doi.org/10.1109/SUMMA48161.2019.8947572.
  • F. Shah, A. Castelltort and A. Laurent, “Extracting Fuzzy Gradual Patterns from Property Graphs,” 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019, pp. 1-6, doi: https://doi.org/10.1109/FUZZ-IEEE.2019.8858936.
  • L. Di-Jorio, A. Laurent and M. Teisseire, “Mining frequent gradual itemsets from large databases” in Advances in Intelligent Data Analysis VIII, Berlin, Heidelberg : Springer Berlin Heidelberg, pp. 297-308, 2009.
  • S. Ayouni, S. Ben Yahia, A. Laurent and P. Poncelet, “Fuzzy gradual patterns: What fuzzy modality for what result?”, SoCPaR: International Conference of Soft Computing and Pattern Recognition, pp. 224-230, 2010, [online] Available: https://hal-lirmm.ccsd.cnrs.fr/lirmm-00798797.
  • H. Koh and E. Hüllermeier, “Mining gradual dependencies based on fuzzy rank correlation”, pp. 379-386, September 29 - October 1, 2010, [online] Available: https://doi.org/https://doi.org/10.1007/978-3-642-14746-3_47.
  • B. Bouchon-Meunier, A. Laurent, M. Lesot and M. Rifqi, “Strengthening fuzzy gradual rules through all the more clauses”, International Conference on Fuzzy Systems, pp. 1-7, July 2010.
  • E. Hüllermeier, “Fuzzy rules in data mining: From fuzzy associations to gradual dependencies”, Combining Experimentation and Theory - A Hommage to Abe Mamdani, vol. 271, pp. 123-135, 2012, [online] Available: https://doi.org/https://doi.org/10.1007/978-3-642-24666-1_9.
  • I. Robinson, J. Webber and E. Eifrem, Graph Databases: New Opportunities for Connected Data, O’Reilly Media, Inc, 2015
  • S. P. Reiss, “Practical data-swapping: the first steps”, ACM Transactions on Database Systems (TODS), pp. 68-73, 1984.
  • Ankit Kumar, Pankaj Dadheech, Vijander Singh, Linesh Raja & Ramesh C. Poonia (2019), “An Enhanced Quantum Key Distribution Protocol for Security Authentication”, Journal of Discrete Mathematical Sciences and Cryptography, 22:4, 499-507, DOI: https://doi.org/10.1080/09720529.2019.1637154.
  • Ankit Kumar, Pankaj Dadheech, Vijander Singh, Ramesh C. Poonia & Linesh Raja (2019), “An Improved Quantum Key Distribution Protocol for Verification”, Journal of Discrete Mathematical Sciences and Cryptography, 22:4, 491-498, DOI: https://doi.org/10.1080/09720529.2019.1637153.
  • Ankit Kumar and Madhavi Sinha (2019), “Design and analysis of an improvedAODVprotocolforblackholeandfloodingattackinvehicular ad-hoc network (VANET)”, Journal of Discrete Mathematical Sciences and Cryptography, 22:4, 453-463, DOI: https://doi.org/10.1080/09720529.2019.1637151.
  • Y. Lindell, “Secure multiparty computation for privacy preserving data mining” in Encyclopedia of Data Warehousing and Mining, IGI Global, pp. 1005-1009, 2005.
  • A. Castelltort and A. Laurent, “Exploiting nosql graph databases and in memory architectures for extracting graph structural data summaries”, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, vol. 25, pp. 81-110, 2017 doi: https://doi.org/10.1142/S0218488517500040

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