5 papers
Learning Defensive Policies against Diverse Inference Attacks for Smart Meter Privacy
Ruichang Zhang, Mustafa A. Mustafa
Smart meter (SM) data provides fine-grained visibility into household energy consumption, but also exposes users to privacy risks. Inference attacks, known as non-intrusive load mo…
FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
Fatima Z. Abacha, Sin G. Teo, Yuanxiang Wu +2
Federated Learning remains highly susceptible to backdoor attacks--malicious clients inject targeted behaviours into the global model. Existing defenses suffer from substantial fal…
Specification Vibing for Automated Program Repair
Taohong Zhu, Lucas C. Cordeiro, Mustafa A. Mustafa +1
Large language model (LLM)-driven automated program repair (APR) has advanced rapidly, but most methods remain code-centric: they directly rewrite source code and thereby risk hall…
Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
Chibueze Peace Obioma, Youcheng Sun, Mustafa A. Mustafa
Federated learning (FL) remains highly vulnerable to adaptive backdoor attacks that preserve stealth by closely imitating benign update statistics. Existing defenses predominantly…
Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning
Ruichang Zhang, Youcheng Sun, Mustafa A. Mustafa
Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through…