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Differential privacy protection algorithm for network sensitive information based on singular value decomposition.

Xuan Ma¹, Xing Chang², Hongxiu Chen³

¹College of Physics Science and Technology, Shenyang Normal University, Shenyang, 110034, Liaoning, China.

Scientific Reports

|April 13, 2023

Summary

This summary is machine-generated.

This study introduces a novel differential privacy algorithm using singular value decomposition to protect sensitive network information. The method enhances data privacy while maintaining data availability for network analysis.

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Area of Science:

Computer Science
Information Security
Data Privacy

Background:

Network sensitive information poses risks of data privacy disclosure.
Existing information privacy protection methods require enhancement.
Differential privacy is a key technique for safeguarding data.

Purpose of the Study:

To propose a differential privacy protection algorithm for network sensitive information.
To improve the effectiveness of information privacy protection and reduce disclosure risks.
To leverage singular value decomposition for robust privacy preservation.

Main Methods:

Utilizing TF-IDF for network sensitive information text extraction.
Improving equal difference privacy budget allocation based on decision tree theory.
Applying singular value decomposition (SVD) with Gaussian noise addition for data disturbance.
Employing random projection for dimensionality reduction of high-dimensional network graphs.

Main Results:

The proposed algorithm effectively reduces the risk of data privacy disclosure.
High privacy protection quality was demonstrated through experimental results.
Data availability was effectively improved, retaining the original data structure.
The method successfully achieves differential privacy for network sensitive information.

Conclusions:

The singular value decomposition-based differential privacy algorithm offers a robust solution for network data protection.
The integration of TF-IDF and improved privacy budget allocation enhances the algorithm's performance.
The approach balances strong privacy guarantees with practical data utility.