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Dual-Algorithm Framework for Privacy-Preserving Task Scheduling Under Historical Inference Attacks.

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Název: Dual-Algorithm Framework for Privacy-Preserving Task Scheduling Under Historical Inference Attacks.
Autoři: Chen, Exiang, Ye, Ayong, Deng, Huina
Zdroj: Computers (2073-431X); Dec2025, Vol. 14 Issue 12, p558, 27p
Témata: EDGE computing, REINFORCEMENT learning, ADAPTIVE control systems, ALGORITHMS, DATA security, MOBILE computing, SCHEDULING
Abstrakt: Historical inference attacks pose a critical privacy threat in mobile edge computing (MEC), where adversaries exploit long-term task and location patterns to infer users' sensitive information. To address this challenge, we propose a privacy-preserving task scheduling framework that adaptively balances privacy protection and system performance under dynamic vehicular environments. First, we introduce a dynamic privacy-aware adaptation mechanism that adjusts privacy levels in real time according to vehicle mobility and network dynamics. Second, we design a dual-algorithm framework composed of two complementary solutions: a Markov Approximation-Based Online Algorithm (MAOA) that achieves near-optimal scheduling with provable convergence, and a Privacy-Aware Deep Q-Network (PAT-DQN) algorithm that leverages deep reinforcement learning to enhance adaptability and long-term decision-making. Extensive simulations demonstrate that our proposed methods effectively mitigate privacy leakage while maintaining high task completion rates and low energy consumption. In particular, PAT-DQN achieves up to 14.2% lower privacy loss and 19% fewer handovers than MAOA in high-mobility scenarios, showing superior adaptability and convergence performance. [ABSTRACT FROM AUTHOR]
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Abstrakt:Historical inference attacks pose a critical privacy threat in mobile edge computing (MEC), where adversaries exploit long-term task and location patterns to infer users' sensitive information. To address this challenge, we propose a privacy-preserving task scheduling framework that adaptively balances privacy protection and system performance under dynamic vehicular environments. First, we introduce a dynamic privacy-aware adaptation mechanism that adjusts privacy levels in real time according to vehicle mobility and network dynamics. Second, we design a dual-algorithm framework composed of two complementary solutions: a Markov Approximation-Based Online Algorithm (MAOA) that achieves near-optimal scheduling with provable convergence, and a Privacy-Aware Deep Q-Network (PAT-DQN) algorithm that leverages deep reinforcement learning to enhance adaptability and long-term decision-making. Extensive simulations demonstrate that our proposed methods effectively mitigate privacy leakage while maintaining high task completion rates and low energy consumption. In particular, PAT-DQN achieves up to 14.2% lower privacy loss and 19% fewer handovers than MAOA in high-mobility scenarios, showing superior adaptability and convergence performance. [ABSTRACT FROM AUTHOR]
ISSN:2073431X
DOI:10.3390/computers14120558