Afsar Ahmed

B. M. Taslimul Haq, Md Arifur Rahman, Tawfiq Al Islam Foysal et al.

Privacy-preserving clustering is critical for analyzing sensitive data in healthcare, cybersecurity, and enterprise applications, where maintaining data confidentiality must be balanced with analytical performance. This paper presents Equivariant Quantum Clustering (EQC), a parameter-efficient framework that integrates symmetry-aware quantum circuits with differential privacy to improve the privacy-utility tradeoff. EQC employs p4m equivariant parameter sharing to reduce circuit complexity while preserving informative feature representations. The framework is evaluated on three privacy-sensitive datasets: NSL-KDD, CERT Insider Threat v6.2, and a synthetic MIMIC-III clinical dataset. On the NSL-KDD benchmark, EQC achieves 79.3% clustering accuracy while reducing membership inference attack success to 38.3% under a privacy budget of ε = 1.0 and δ = 10^-5, outperforming representative classical and quantum baselines. Ablation studies indicate that the performance gains primarily arise from parameter-efficient circuit design combined with differential privacy. The results demonstrate that EQC provides a practical quantum-ready framework for secure and privacy-preserving clustering across heterogeneous sensitive datasets.

Attia Qammar, Qazi Haseeb Yousaf, Ali Azam et al.

Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sustainable development strategies. Existing forecasting approaches typically rely on centralized data collection, which is often restricted by privacy regulations and the distributed nature of emission data across countries and industrial sectors. This paper proposes a novel federated hybrid forecasting framework that integrates ARIMA-based trend modeling, GARCH-based volatility modeling, LSTM-Attention temporal representation learning, and XGBoost prediction within a privacy-preserving federated learning environment. The proposed framework enables collaborative learning among distributed clients without requiring the exchange of raw data. Experimental evaluation across 14 clients demonstrates strong forecasting performance, achieving client R2 values between 0.50 and 0.97 with an average of 0.73, RMSE values ranging from 0.06 to 2.35 with an average of 1.21, and MAPE values between 1.5% and 11.3% with an average of 6.5%. The results indicate that the proposed framework provides an accurate, scalable, and regulation-compliant solution for collaborative carbon-emission forecasting.

Arif Ahmed, Gondy Leroy, Agrim Sachdeva et al.

Generative artificial intelligence is increasingly used for health information, but inaccurate outputs raise concerns about trust calibration and overreliance. This study examines whether learned dependency on generative artificial intelligence affects trust in AI-generated health information and whether visual attention cues reduce overtrust in incorrect outputs. We conducted a randomized 2 by 2 experiment with 338 participants, manipulating information accuracy and visual attention cues. Trust and dependency were measured using survey scales, and linear regression models tested main and interaction effects. Information accuracy increased trust, and learned dependency was positively associated with trust. The interaction between accuracy and dependency was significant, indicating weaker trust calibration among highly dependent users. Visual attention cues did not significantly affect trust or moderate the effect of dependency. The findings suggest that learned dependency weakens trust calibration and increases susceptibility to incorrect AI-generated health information.