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Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation.

Manikandan Thirumalaisamy1, Shajahan Basheer1, Shitharth Selvarajan2

  • 1School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India.

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Summary
This summary is machine-generated.

This study introduces the Averaged One-Dependence Estimators (AODE) and SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) methods to manage cloud infrastructure challenges. These techniques improve data separation and reduce energy consumption and makespan time in smart city data management.

Keywords:
AODE classifiercloud computing separationdata classificationdata obfuscationdata storingdata transmission

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Area of Science:

  • Cloud Computing
  • Data Management
  • Cybersecurity

Background:

  • Managing cloud infrastructure presents inherent challenges in software, legality, and contract-based segmentation.
  • Existing methods struggle with efficient data handling and security in cloud platforms.
  • Smart city data management requires robust solutions for privacy and performance.

Purpose of the Study:

  • To propose feasible solutions for inherent issues in cloud infrastructure and platform management.
  • To introduce the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method for data separation.
  • To enhance data security and efficiency in smart city data through hybrid data obfuscation.

Main Methods:

  • The proposed methodology combines the AODE classifier with the SELECT-APSL ASA method.
  • A hybrid data obfuscation technique is employed, masking 50% of raw data and 50% of hospital data.
  • AODE is utilized to separate smart city data based on the hybrid obfuscation technique.

Main Results:

  • Energy consumption analysis shows a significant reduction after cryptosystem assumption (47.34%) compared to existing algorithms.
  • Average energy consumption decreased by 2.47% before obfuscation and 9.90% after obfuscation.
  • Makespan time decreased by 33.71% before obfuscation and 1.3% after obfuscation, demonstrating improved performance.

Conclusions:

  • The proposed AODE and SELECT-APSL ASA methodology effectively addresses cloud infrastructure management challenges.
  • The hybrid data obfuscation technique significantly enhances data security and reduces processing times.
  • The study demonstrates the strength and efficiency of the proposed methods for smart city data management.