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Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing.

Dandan Ma¹, Dequan Kong¹, Xiaowei Chen¹

¹School of Information and Electronic Technology, Jiamusi University, Jiamusi, China.

Frontiers in Neurorobotics

|October 17, 2022

Summary

This study introduces a novel privacy protection method for mobile crowdsensing using a Q-learning particle swarm optimization algorithm. It effectively safeguards user location data by generalizing tasks and minimizing privacy risks.

Keywords:

Q-learning RLBS crowdsensing service location privacy protection particle swarm optimization

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Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Mobile intelligent devices generate vast amounts of location data, posing privacy risks.
Existing privacy protection strategies are inadequate for mobile crowdsensing environments.
Crowdsensing services exacerbate personal privacy leakage concerns.

Purpose of the Study:

To propose a novel location privacy protection method for mobile crowdsensing.
To enhance user location data security against malicious third parties.
To address the limitations of current privacy strategies in crowdsensing.

Main Methods:

A novel location privacy protection algorithm based on Q-learning and particle swarm optimization.
Task generalization to obscure specific user activities and break user-task associations.
Q-learning to train a confounding task scheme with a low rejection rate, optimized by particle swarm optimization.

Main Results:

The proposed scheme demonstrates good performance in privacy budget error, availability, and cloud timeliness.
Significantly improves the security of user location data in mobile crowdsensing.
Achieves an inhibition ratio close to the optimal value, enhancing privacy protection.

Conclusions:

The Q-learning particle swarm optimization algorithm offers an effective solution for location privacy in mobile crowdsensing.
Task generalization is a viable strategy to protect user location privacy without compromising service availability.
The developed method significantly enhances the security and privacy of location data within crowdsensing frameworks.