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An efficient reversible privacy-preserving data mining technology over data streams.

Chen-Yi Lin1, Yuan-Hung Kao2, Wei-Bin Lee2

  • 1Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan.

Springerplus
|September 10, 2016
PubMed
Summary

This study introduces a continuous reversible privacy-preserving (CRP) algorithm for real-time data streams. CRP offers enhanced data security, knowledge preservation, and efficiency for smart devices.

Keywords:
Cloud computingData protectionData streamsSliding window

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Area of Science:

  • Computer Science
  • Data Security
  • Cloud Computing

Background:

  • Increasing use of smart devices and cloud computing raises concerns about private data security.
  • Data streams require efficient and real-time privacy-preserving solutions.

Purpose of the Study:

  • To design a novel reversible privacy-preserving algorithm for continuous data streams.
  • To ensure data integrity and minimize privacy risks in real-time data processing.

Main Methods:

  • Developed the continuous reversible privacy-preserving (CRP) algorithm using a sliding window approach.
  • Integrated an embedded watermark for data integrity verification.
  • Designed a data recovery process for accurate data restoration.

Main Results:

  • CRP demonstrated superior knowledge preservation compared to existing algorithms.
  • CRP effectively reduced information loss and privacy disclosure risks.
  • CRP processing time for continuous data was significantly lower than existing methods.

Conclusions:

  • The CRP algorithm is highly suitable for data stream environments.
  • CRP meets the lightweight and energy-efficient requirements for smart handheld devices.
  • CRP provides a robust solution for real-time data security and integrity.