5 papers
Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors
Niraj Kumar, Harsh Kasyap
Post-hoc model explainers such as LIME, SHAP, and Integrated Gradients are widely deployed to audit models in high-stakes sensitive domains, including finance, healthcare, and soci…
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca +2
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditiona…
CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning
Arnab Nath, Harsh Kasyap
Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Sec…
Analysis of LLMs Against Prompt Injection and Jailbreak Attacks
Piyush Jaiswal, Aaditya Pratap, Shreyansh Saraswati +2
Large Language Models (LLMs) are widely deployed in real-world systems. Given their broader applicability, prompt engineering has become an efficient tool for resource-scarce organ…
Fairness-Constrained Optimization Attack in Federated Learning
Harsh Kasyap, Minghong Fang, Zhuqing Liu +2
Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL enables model sharing, while re…