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Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms.

Rafik Hamza1,2, Alzubair Hassan3,4, Awad Ali5

  • 1Institute for International Strategy, Tokyo International University, Saitama 350-1197, Japan.

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Homomorphic encryption enables computations on encrypted Big Data, overcoming limitations of traditional methods. This study overviews tools and frameworks for secure Big Data analysis and privacy-preserving machine learning.

Keywords:
big dataencryption algorithmshomomorphic encryptionmachine learningprivacy preserving

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Area of Science:

  • Computer Science
  • Cryptography
  • Data Security

Background:

  • Traditional encryption like Advanced Encryption Standard (AES) requires decryption for computation, compromising privacy.
  • Homomorphic encryption (HE) allows computations directly on encrypted data, preserving information confidentiality.
  • Existing HE algorithms face challenges like high computational cost and case-specific modifications.

Purpose of the Study:

  • To provide a comprehensive overview of homomorphic encryption tools for Big Data analysis.
  • To discuss a security framework for privacy-preserving Big Data analytics using HE.
  • To highlight features, tradeoffs, and performance comparisons of HE tools for practical applications.

Main Methods:

  • Literature review of homomorphic encryption tools and frameworks.
  • Analysis of security considerations for Big Data processing with HE.
  • Comparative evaluation of popular HE toolkits based on features and performance metrics.

Main Results:

  • Identified key features and tradeoffs for selecting HE tools in Big Data.
  • Compared the performance of various homomorphic encryption toolkits through implementation results.
  • Presented a security framework for privacy-preserving Big Data analysis using HE algorithms.

Conclusions:

  • Homomorphic encryption offers a promising solution for secure Big Data processing and privacy-preserving machine learning.
  • Careful consideration of HE tool characteristics and performance is crucial for practical Big Data applications.
  • Further research is needed to address limitations and enhance the utility and performance of HE in Big Data analytics.