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Privacy-preserving parallel kNN classification algorithm using index-based filtering in cloud computing.

Yong-Ki Kim1, Hyeong-Jin Kim2, Hyunjo Lee2

  • 1Dept. of IT Convergence System, Vision College of Jeonju, Jeonju, Jeollabuk-do, Republic of Korea.

Plos One
|May 5, 2022
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Summary

This study introduces a privacy-preserving parallel kNN classification algorithm for cloud computing. It enhances efficiency for encrypted data mining, protecting sensitive information and improving query processing times.

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Area of Science:

  • Cloud Computing
  • Data Security
  • Artificial Intelligence

Background:

  • Database outsourcing in cloud computing necessitates robust data protection.
  • Existing data mining techniques over encrypted data lack efficiency for large datasets.
  • kNN classification is a key technique in data analysis and AI, but its encrypted application is inefficient.

Purpose of the Study:

  • To propose an efficient privacy-preserving parallel kNN classification algorithm for cloud environments.
  • To address the computational and query processing inefficiencies of existing methods for encrypted data mining.
  • To ensure data, query, and access pattern protection in outsourced databases.

Main Methods:

  • Developed a privacy-preserving parallel kNN classification algorithm.
  • Implemented an improved secure protocol using an encrypted random value pool to reduce encryption computation cost.
  • Integrated a garbled circuit to decrease query processing time.
  • Conducted a security analysis to validate data, query, and access pattern protection.

Main Results:

  • The proposed algorithm significantly reduces computation costs for encryption.
  • Query processing time is substantially improved through parallelization and garbled circuits.
  • Performance evaluation demonstrates a 2 to 25 times improvement over existing algorithms.
  • The algorithm successfully provides robust protection for data, queries, and access patterns.

Conclusions:

  • The proposed privacy-preserving parallel kNN classification algorithm offers a significant advancement in efficient and secure encrypted data mining.
  • This method is highly effective for handling large-scale encrypted datasets in cloud environments.
  • The algorithm provides a practical solution for secure database outsourcing, balancing performance and privacy.