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

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.