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Related Concept Videos

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Related Experiment Videos

Chaos-Based Simultaneous Compression and Encryption for Hadoop.

Muhammad Usama1, Nordin Zakaria1

  • 1HPCC Service Center, Department of Computer & Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Tronoh, Perak, Malaysia.

Plos One
|January 11, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for simultaneous data compression and encryption within the Hadoop framework. The approach enhances security and efficiency by integrating chaotic maps and a stealth key.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Information Security
  • Data Storage

Background:

  • Data compression and encryption are essential for platforms like Hadoop.
  • Current methods typically apply compression and encryption sequentially, which can be inefficient.
  • Existing source coding methods face implementation challenges with real number precision.

Purpose of the Study:

  • To propose an efficient data storage method that couples compression and encryption in Hadoop.
  • To address the infinite precision issue in chaotic map-based source coding.
  • To enhance encryption quality and maintain compression capabilities.

Main Methods:

  • Developed a simultaneous compression and encryption scheme for the Hadoop framework.
  • Utilized Tent Map and Piece-wise Linear Chaotic Map (PWLM) for source coding.
  • Implemented a solution to remove fractional components from long products of real numbers.
  • Incorporated a stealth key for cyclic shifts in PWLM and a masking pseudorandom keystream.

Main Results:

  • The proposed algorithm efficiently couples compression and encryption.
  • The method resolves the infinite precision issue in chaotic map implementations.
  • The stealth key and masking keystream enhance security without sacrificing compression.
  • The algorithm integrates seamlessly with the Hadoop framework.

Conclusions:

  • The proposed simultaneous compression and encryption scheme offers an efficient solution for Hadoop.
  • The approach provides robust security and effective compression for data storage.
  • This method overcomes key implementation challenges in chaotic map-based coding.