The paper proposes Family-Grouped Hierarchical Federated Learning (Family-FL) combined with a highly optimized Tiny CNN-LSTM model to enable privacy-preserving ECG monitoring on ultra-resource-constra…

This paper proposes a novel Simultaneous Data Compression and Encryption (SDCE) system that combines chaotic map-based encryption with Huffman encoding to securely and efficiently transmit large video…

The paper introduces ArrythML, a highly efficient autoencoder-based TinyML model that enables accurate, low-power arrhythmia detection directly on resource-constrained embedded wearable devices.

The paper proposes a novel cross-layer authentication framework for healthcare information exchange that combines initial PKI-based verification with continuous, lightweight physical layer feature ext…

This paper conducts an extensive microbenchmark study to characterize the performance of core cryptographic workloads across various cloud services, architectures, and programming languages, identifyi…

The paper proposes EnThM, a lightweight, hierarchical verification scheme that uses statistical and rule-based checks on aggregated metering data to mitigate real-time power theft in smart grids.

The paper proposes a comprehensive, phased hybrid migration framework to transition vulnerable IoT-based healthcare systems to quantum-safe cryptography.

mmFHE introduces the first system enabling end-to-end mmWave radar sensing using fully homomorphic encryption (FHE), allowing sensitive data processing on untrusted cloud infrastructure while maintain…

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 an attention-guided hybrid framework combining 1D and 2D CNNs to robustly enhance ECG-based biometric recognition, achieving high accuracy across multiple datasets and demonstrating…

The paper proposes a lightweight Zero-Knowledge authentication protocol using QR codes, enhancing the Schnorr protocol with nonces and timestamps for secure, efficient, and replay-attack-resistant aut…

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 a Sovereign AI architecture for clinical triage that ensures maximum security by performing all inference on-device and receiving data only through physically unidirectional channel…

This paper proposes a novel data-driven image encryption framework that learns the chaotic map dynamics directly from the image data, enhancing security beyond traditional fixed-map schemes.

The paper proposes TAAC, a novel framework that enables accurate depression detection from audio while ensuring user privacy by selectively encrypting sensitive identity information.

This paper proposes a comprehensive framework utilizing AI and machine learning to enhance cybersecurity and mitigate fraud risks in the emerging field of cardless artificial intelligence banking.

This paper presents BenDi, an energy-efficient quasi-stochastic systolic architecture for bioelectronic systems on the edge.

DSTAN-Med is a novel dual-channel attention framework that significantly improves False Data Injection (FDI) attack detection in IoMT medical devices by explicitly separating spatial and temporal depe…

The paper proposes a generic, standard model construction for Anamorphic Key Encapsulation Mechanisms (AKEM) that achieves strong IND-CCA security, addressing a major gap in covert communication crypt…

This paper proposes a novel, on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for IoMT environments, achieving high classification accuracy while providing transpar…