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…

This survey comprehensively analyzes the IoT threat landscape by detailing 28 common attacks and mapping them to foundational vulnerability classes, providing a structured roadmap for building secure…

The paper addresses the over-reliance on GDPR in digital privacy research by systematically normalizing heterogeneous global data protection laws into a unified, data-lifecycle-aligned abstraction.

This survey analyzes privacy-enhancing encryption technologies (ABE, PRE, SE) for data sharing, proposing a comprehensive framework, identifying potential attacks, and evaluating their multi-dimension…

This multivocal literature review analyzes the convergence of IoT and Zero Trust security, finding that academia focuses on IoT modifications while industry prioritizes practical integration within ex…

This review comprehensively analyzes state-of-the-art decentralized trust and security mechanisms, concluding that while these approaches enhance privacy and resilience for IoT edge networks, challeng…

This paper provides the first comprehensive review of threats and defenses specifically targeting on-device AI inference, revealing a significant imbalance where certain attack types, like adversarial…

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

The paper proposes a comprehensive, dual-layer architectural framework for AI identification and traceability, ensuring continuous accountability and regulatory oversight throughout the entire lifecyc…

The paper proposes the User Data Sharing System (UDSS), a hardware-anchored middleware that securely manages PII exchange across diverse consumer electronics devices, significantly reducing onboarding…

This paper synthesizes the emerging field of blockchain and AI for securing intelligent networks by providing a comprehensive taxonomy, integration patterns, and an evaluation blueprint.

This paper provides a systematic regulatory mapping and compliance architecture for AI agents operating under the complex web of EU laws, concluding that high-risk agents with untraceable behavioral d…

This paper reviews recent EU AI regulatory documents to clarify definitions and synthesize current provisions regarding security, privacy, and autonomous agentic AI.

This paper proposes a comprehensive, risk-based auditing framework designed to help internal and external auditors assess the cybersecurity risks posed by diverse IoT devices within corporate and indu…

This paper systematically identifies long-term operational risks associated with smart household appliances, using the smart fridge as a case study, and finds that even basic functions are vulnerable…

This paper proposes and evaluates the integration of Federated Learning and blockchain technology over cloud-edge infrastructure to enhance data privacy and security for decentralized AI applications.

This paper conducts a literature review of non-academic publications to consolidate current knowledge, trends, and future challenges regarding the industrial integration of IoT devices within a Zero T…

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 study proposes a negotiation framework, using composite indices (RBTI and CATI), to explain how youth navigate competing privacy pressures when using smart voice assistants, finding that high usa…

The paper reverse-engineers Apple's Private Cloud Compute (PCC) implementation to independently benchmark its model and evaluate its privacy claims, addressing the lack of transparency in Apple's syst…