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  11. Mining Privacy-preserving Association Rules Using Transaction Hewer Allocator And Facile Hash Algorithm In Multi-cloud Environments

Mining privacy-preserving association rules using transaction hewer allocator and facile hash algorithm in multi-cloud environments

D Dhinakaran1, S Gopalakrishnan2, D Selvaraj3

  • 1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.

Methodsx
|May 7, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a multi-cloud privacy framework using the Facile Hash Algorithm (FHA) and Transaction Hewer and Allocator for secure frequent itemset mining. The ATid algorithm enhances scalability and reduces computational time and communication costs.

Area of Science:

  • Computer Science
  • Data Security
  • Cloud Computing

Background:

  • Data-driven decision-making requires secure extraction of knowledge from distributed datasets.
  • Privacy concerns arise when outsourcing data for tasks like frequent itemset mining.
  • Balancing data sensitivity with insight delivery is a key challenge.

Purpose of the Study:

  • To propose a novel multi-cloud privacy framework for secure frequent itemset mining.
  • To enhance the efficiency and scalability of privacy-preserving data mining.
  • To address privacy during data transmission and computation across distributed cloud platforms.

Main Methods:

  • A multi-cloud privacy framework incorporating the Transaction Hewer and Allocator module and the Facile Hash Algorithm (FHA).
  • Introduction of the Apriori with Tid Reduction (ATid) algorithm to improve scalability and computational efficiency.
Keywords:
Association rule miningData encryptionData privacyDistributed data analysis

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  • Performance evaluation using several datasets to compare with existing methods.

    • Main Results:

    • The proposed framework demonstrates higher performance compared to existing methods.
    • Encryption and decryption processes reduce computational time by up to 25%.
    • Communication costs are reduced by approximately 15%, with improved scalability and reduced communication overhead.

    Conclusions:

    • The multi-cloud privacy framework effectively protects data during transmission and computation.
    • The ATid algorithm significantly enhances scalability and operational efficiency in frequent itemset mining.
    • The proposed approach offers a secure, efficient, and scalable solution for distributed data mining.
    Facile Hash Algorithm, Transaction Hewer and Allocator, and ATid algorithm
    Frequent itemset mining
    Horizontal partitioning