Abstract In the Internet of Medical Things (IoMT), the vulnerability of federated learning (FL) to single points of failure, low‐quality nodes, and poisoning attacks necessitates innovative solutions. This article introduces a FL‐driven dual‐blockchain approach to address these challenges and improve data sharing and reputation management. Our approach comprises two blockchains: the Model Quality Blockchain (MQchain) and the Reputation Incentive Blockchain (RIchain). MQchain utilizes an enhanced Proof of Quality (PoQ) consensus algorithm to exclude low‐quality nodes from participating in aggregation, effectively mitigating single points of failure and poisoning attacks by leveraging node reputation and quality thresholds. In parallel, RIchain incorporates a reputation evaluation, incentive mechanism, and index query mechanism, allowing for rapid and comprehensive node evaluation, thus identifying high‐reputation nodes for MQchain. Security analysis confirms the theoretical soundness of the proposed method. Experimental evaluation using real medical datasets, specifically MedMNIST, demonstrates the remarkable resilience of our approach against attacks compared to three alternative methods.

Generative Artificial Intelligence (GenAI) is transforming the workplace, but its adoption introduces significant risks to data security and privacy. Recent incidents underscore the urgency of addressing these issues. This comprehensive survey investigates the implications of GenAI integration in workplaces, focusing on its impact on organizational operations and security. We analyze vulnerabilities within GenAI systems, threats they face, and repercussions of AI-driven workplace monitoring. By examining diverse attack vectors like model attacks and automated cyberattacks, we expose their potential to undermine data integrity and privacy. Unlike previous works, this survey specifically focuses on the security and privacy implications of GenAI within workplace settings, addressing issues like employee monitoring, deepfakes , and regulatory compliance. We delve into emerging threats during model training and usage phases, proposing countermeasures such as differential privacy for training data and robust authentication for access control. Additionally, we provide a comprehensive analysis of evolving regulatory frameworks governing AI tools globally. Based on our comprehensive analysis, we propose targeted recommendations for future research and policy-making to promote responsible and secure adoption of GenAI in the workplace, such as incentivizing the development of explainable AI (XAI) and establishing clear guidelines for ethical data usage. This survey equips stakeholders with a comprehensive understanding of GenAI’s complex workplace landscape, empowering them to harness its benefits responsibly while mitigating risks.

Identity sharing systems, regardless of their architectural models, share common vulnerabilities. These systems compel users to divulge personal information and furnish proof of identity for accessing services, leaving them susceptible to data breaches that can culminate in identity theft and jeopardize online data security. While blockchain technology offers a potential remedy, delivering enhanced security, immutability, and traceability, it simultaneously raises pertinent concerns surrounding privacy and transparency. The integration of zero-knowledge proof (ZKP) technology has emerged as a promising solution, particularly in enhancing privacy within the transparent blockchain ecosystem. Our paper conducts an exhaustive survey of the existing literature, with a particular focus on the assimilation of ZKP technology into blockchain for the secure sharing of user identities. We undertake a critical evaluation of the advancements achieved in this domain, pinpoint the formidable challenges that must be confronted, and uncover nascent opportunities for further exploration. Our contribution transcends the realms of mere summarization and analysis; we go a step further by offering recommendations drawn from real-world case studies and delineating future research directions.

In recent years, several blockchain-based models have emerged to provide a secure way to store and access sensitive electronic medical records (EMRs) across the healthcare sector. These records are of different priorities and business requirements. From our comprehensive literature review, we observe that the existing models have no provision of prioritizing the EMR transactions. This critically affects the quick and streamline sharing of emergency EMRs in a smart healthcare environment. Furthermore, the lack of prioritization significantly restricts the optimal usage of the blockchain network. Motivated by this, we first propose a lightweight and deterministic method to prioritize the flow of emergency healthcare transactions through the smart contract. We also propose logical stateless transaction models for different entities involved in the system with varying levels of trust. Finally, the performance of the model based on the private Ethereum is verified and it outperforms the existing benchmark model in terms of usefulness in the healthcare setting and computation overhead with the use of a simple prioritizing algorithm. The obtained results demonstrate the feasibility of the proposed scheme in the real-time smart healthcare system.

In current healthcare systems, electronic medical records (EMRs) are always located in different hospitals and controlled by a centralized cloud provider. However, it leads to single point of failure as patients being the real owner lose track of their private and sensitive EMRs. Hence, this article aims to build an access control framework based on smart contract, which is built on the top of distributed ledger (blockchain), to secure the sharing of EMRs among different entities involved in the smart healthcare system. For this, we propose four forms of smart contracts for user verification, access authorization, misbehavior detection, and access revocation, respectively. In this framework, considering the block size of ledger and huge amount of patient data, the EMRs are stored in cloud after being encrypted through the cryptographic functions of elliptic curve cryptography (ECC) and Edwards-curve digital signature algorithm (EdDSA), while their corresponding hashes are packed into blockchain. The performance evaluation based on a private Ethereum system is used to verify the efficiency of proposed access control framework in the real-time smart healthcare system.