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FLPP: A Federated-Learning-Based Scheme for Privacy Protection in Mobile Edge Computing.

Zhimo Cheng¹, Xinsheng Ji^1,2, Wei You¹

¹Department of Next-Generation Mobile Communication and Cyber Space Security, Information Engineering University, Zhengzhou 450002, China.

Entropy (Basel, Switzerland)

|November 24, 2023

Summary

This study introduces a federated learning privacy-protection scheme (FLPP) to balance data security and accuracy in mobile edge computing. FLPP enhances overall performance by dynamically adjusting privacy levels for secure and accurate data sharing.

Keywords:

differential evolutionary differential privacy federated learning mobile edge computing privacy protection

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Area of Science:

Mobile Edge Computing
Data Privacy
Federated Learning

Background:

Data sharing in mobile edge computing (MEC) is crucial for innovation but faces significant data privacy risks.
Existing methods like federated learning (FL) protect data by transmitting model parameters, not raw data, and use privacy-enhancing techniques (PETs) against inference attacks.
However, PETs can decrease training accuracy, creating a trade-off between security and data utility, especially in dynamic MEC environments.

Purpose of the Study:

To propose a novel federated-learning-based privacy-protection scheme (FLPP) to address the challenge of balancing data security and accuracy in MEC.
To develop a layered adaptive differential privacy model for dynamic privacy-level adjustment.
To design a differential evolutionary algorithm for optimizing privacy-protection policies.

Main Methods:

Federated learning (FL) framework for secure data processing.
Layered adaptive differential privacy model for dynamic privacy control.
Differential evolutionary algorithm for privacy policy optimization.

Main Results:

The proposed FLPP scheme demonstrates an 8-34% advantage in overall performance compared to existing methods.
The layered adaptive differential privacy model effectively adjusts privacy levels based on situational needs.
The differential evolutionary algorithm successfully identified optimal privacy policies.

Conclusions:

The FLPP scheme effectively enables secure and accurate data sharing in mobile edge computing environments.
The proposed approach successfully mitigates the trade-off between data security and training accuracy.
This research contributes a practical solution for privacy-preserving data analysis in dynamic MEC systems.