The paper proposes a novel four-phase protocol to enable secure, multi-key homomorphic encryption (xMK-CKKS) aggregation for zero-order Federated Learning over wireless channels without requiring chan…

This paper introduces a unified threat model and evaluation framework to systematically compare privacy-preserving techniques for distributed learning in IoT systems, highlighting the trade-off betwee…

The paper proposes Q-FE, a novel Quantum-Native 6G Far-Edge architecture that secures Industrial IoT Digital Twins by integrating micro-digital twins, compact post-quantum key exchange, and asynchrono…

LiteAtt introduces a verifier-less, Peer-to-Peer Self-Attestation (P2P-SA) framework for modern IoT MCUs, enabling mutual authentication and firmware attestation directly within the connection handsha…

The paper characterizes Homomorphic Encryption (HE) operations on a real-world Processing-In-Memory (PIM) system, demonstrating that while PIM is a viable alternative to CPUs/GPUs, performance is limi…

The paper proposes VerFU, a client-verifiable federated unlearning framework for low-altitude wireless networks that allows devices to ensure the server accurately removes their historical data contri…

SecureAFL introduces a robust framework to secure asynchronous Federated Learning against poisoning attacks by detecting anomalous updates, estimating missing client contributions, and using Byzantine…

This paper proposes a lightweight, multi-layer Machine Learning-based security framework for Industrial IoT (IIoT) to enhance trust convergence and detect advanced threats.

CLAD is a federated learning framework that jointly performs anomaly detection and attack classification in heterogeneous IoT environments by combining clustered learning with a dual-mode architecture…

CHRONOS is a novel three-layer architecture designed to address coupled failures in temporal data marketplaces by integrating temporal decay, changepoint-aware pricing, and differential privacy for ro…

EdgeDetect is a communication-efficient and privacy-preserving federated intrusion detection system that uses gradient binarization and homomorphic encryption to significantly reduce bandwidth usage w…

The paper proposes a secure and verifiable aggregation scheme for Federated Learning using a non-colluding dual-server architecture and linear tags, which significantly enhances user privacy and reduc…

The paper introduces $I$-$(OT)^2$, a novel base 1-out-of-2 Oblivious Transfer (OT) protocol designed to minimize computation and interaction for resource-constrained IoT devices.

The paper proposes TimeGuard, a novel channel-wise pool training defense, to significantly improve the robustness of time series forecasting against backdoor attacks by addressing signal dilution and…

This paper provides a comprehensive, system-level taxonomy for designing quantum-resistant network architectures, moving beyond simple protocol substitutions to address key distribution and management…

This paper presents a novel data-free Membership Inference Attack (MIA) that uses gradient inversion on Standard Cell Library Layouts (SCLLs) to reconstruct sensitive hardware images from intercepted…

The paper proposes a revocation-ready key management layer for blockchain-based IoT data sharing that replaces online key release with ciphertext key publication, enabling local, policy-based decrypti…

This paper provides a comparative analysis and benchmarking of Secure Multi-Party Computation (SMPC) and Fully Homomorphic Encryption (FHE) for machine learning, finding that the optimal choice depend…

The paper proposes a novel triple-hoisted baby-step giant-step algorithm and a memory-optimized FPGA accelerator to significantly reduce the ciphertext rotations and off-chip memory access latency whe…

The paper proposes DIST-FL, a distributed system using multiple TEEs and an append-only ledger to enhance the security and robustness of federated learning aggregation against server-side adversaries.