PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning
PEARL (Personalized Early-exit Adaptive Reinforcement Learning) reduces adversarial state-inference accuracy by 25.67% on average with a controlled 10-16% utility cost, establishing a practical, dynamically enforceable privacy-utility tradeoff.
Mojtaba Taherisadr, Salma Elmalaki
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