The metaverse is touted as an exciting new technology amalgamation facilitating next-level immersive experiences for users. However, initial experiences indicate that a host of privacy, security and control issues will need to be effectively resolved for its vision to be realized. This paper highlights the security issues that will need to be resolved in the metaverse and the underlying enabling technologies/platforms. It also discussed the broader challenges confronting the developers, the service providers and other stakeholders in the metaverse ecosystem which if left unaddressed may hamper its broad adoption and appeal. Finally, some ideas on building a viable Zero-Trust Architecture (ZTA) model for the metaverse are presented.

/pdf/metaverse-security-issues-challenges-and-a-viable-zta-model-2199ktem.pdf

Metaverse Security: Issues, Challenges and a Viable ZTA Model

Misinformation has been a persistent and harmful phenomenon affecting our society in various ways, including individuals' physical health and economic stability. With the rise of short video platforms and related applications, the spread of multi-modal misinformation, encompassing images, texts, audios, and videos have exacerbated these concerns. The introduction of generative AI models like ChatGPT and Stable Diffusion has further complicated matters, giving rise to Artificial Intelligence Generated Content (AIGC) and presenting new challenges in detecting and mitigating misinformation. Consequently, traditional approaches to misinformation detection and intervention have become inadequate in this evolving landscape. This paper explores the challenges posed by AIGC in the context of misinformation. It examines the issue from psychological and societal perspectives, and explores the subtle manipulation traces found in AIGC at signal, perceptual, semantic, and human levels. By scrutinizing manipulation traces such as signal manipulation, semantic inconsistencies, logical incoherence, and psychological strategies, our objective is to tackle AI-generated misinformation and provide a conceptual design of systematic explainable solution. Ultimately, we aim for this paper to contribute valuable insights into combating misinformation, particularly in the era of AIGC.

Combating Misinformation in the Era of Generative AI Models

In today’s world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange information between medical stakeholders (patients, physicians, insurance companies, pharmaceuticals, medical researchers, etc.) suffer weaknesses in terms of security and privacy due to having centralized architecture. Blockchain technology ensures the privacy and security of EHR systems thanks to the use of encryption. Moreover, due to its decentralized nature, this technology prevents central failure and central attack points. In this paper, a systematic literature review (SLR) is proposed to analyze the existing Blockchain-based approaches for improving privacy and security in electronic health systems. The research methodology, paper selection process, and the search query are explained. 51 papers returned from our search criteria published between 2018 and Dec 2022 are reviewed. The main ideas, type of Blockchain, evaluation metrics, and used tools of each selected paper are discussed in detail. Finally, future research directions, open challenges, and some issues are discussed. 

/pdf/blockchain-based-privacy-and-security-preserving-in-cjvgevlh.pdf

Blockchain-based privacy and security preserving in electronic health: a systematic review

Toward Metaverse of everything: Opportunities, challenges, and future directions of the next generation of visual/virtual communications

In smart communities, social media allowed users easy access to multimedia content. With recent advancements in computer vision and natural language processing, machine learning (ML), and deep learning (DL) models have evolved. With advancements in generative adversarial networks (GAN), it has become possible to create fake images/audio/and video streams of a person or use some person’s audio and visual details to fit other environments. Thus, deepfakes are specifically used to disseminate fake information and propaganda on social circles that tarnish the reputation of an individual or an organization. Recently, many surveys have focused on generating and detecting deepfake images, audio, and video streams. Existing surveys are mostly aligned toward detecting deepfake contents, but the generation process is not suitably discussed. To address the survey gap, the paper proposes a comprehensive review of deepfake generation and detection and the different ML/DL approaches to synthesize deepfake contents. We discuss a comparative analysis of deepfake models and public datasets present for deepfake detection purposes. We discuss the implementation challenges and future research directions regarding optimized approaches and models. A unique case study, IBMM is discussed, which presents a multi-modal overview of deepfake detection. The proposed survey would benefit researchers, industry, and academia to study deepfake generation and subsequent detection schemes.

Deepfake Generation and Detection: Case Study and Challenges

The fake news “infodemic”, facilitated by social media and mobile message sharing platforms, has progressed from causing a nuisance to seriously impacting law and order through deliberate and large-scale manipulation of public sentiments. There are social, religious, political, and economic dimensions to the fake news phenomenon, providing enough motivation for interested parties to push biased opinions, claims, conspiracies and fraud to many naïve information consumers. The ease with which fake news can be created and propagated makes it extremely challenging to detect and mitigate. To combat the fake news, the researchers have utilized mechanisms which are largely based on Artificial Intelligence (AI) algorithms and social network analysis. However, no viable solution has yet been deployed at a scale. This paper present a comprehensive survey on combating fake news and evaluates the challenges involved in its detection with the help of existing detection mechanisms and techniques to control its spread. The challenges associated with combating fake news have been addressed based on the various aspects such as psychological, economic, and technical. Furthermore, we consider the fake news combat spectrum to analyze the stakeholder interventions due to the spread of fake news. Finally, various technology-based solutions have been presented for combating fake news and the associated future challenges and opportunities.

/pdf/combating-fake-news-stakeholder-interventions-and-potential-1tyviijf.pdf

Combating Fake News: Stakeholder Interventions and Potential Solutions

Modern healthcare is a data-intensive domain representing an amalgamation of long-term electronic medical records, real-time patient monitoring data, and more recently sensor data from wearable computing. Blockchain in healthcare can address a multitude of challenges in healthcare, including care coordination, data security, and interoperability concerns, as technology advances. Technical challenges such as processing speed and massive data duplication will be resolved as improved technology. This data needs to be accessed seamlessly by a multitude of players from the general physicians to hospitals, medical service providers to insurance companies. Thus, healthcare-related data needs to be verified, securely stored, and shared while maintaining patient privacy and control over what portion of the data is shared, with whom it is shared, and how it is consumed. Blockchain has emerged as a technology stack of choice for distributed authentication, secure storage, and automated analysis of stored data in diverse domains including healthcare. Its distributed nature is a natural fit to the healthcare ecosystem with multiple participating entities and patients in different geographic locations. In this paper, we review the technology of blockchain to the healthcare domain analyzing and classifying work done in the field. Open challenges are identified and future directions for research are also presented.

Blockchain in Secure Healthcare Systems: State of the Art, Limitations, and Future Directions

Proxy smart contracts for zero trust architecture implementation in Decentralised Oracle Networks based applications

The Metaverse envisions a future where immersive online interactions become the norm. However, this vision can only be safely realized if user safety and privacy are prioritized. This review critically evaluates existing user safety and security measures within the Metaverse. We have highlighted the limited ability of existing solutions to protect against harassment, identity theft, and the misuse of personal data collected in this environment. This research comprehensively analyzes user safety and security dimensions within virtual worlds and presents a taxonomy to classify the unique threats and vulnerabilities Metaverse users may encounter. This review reveals critical gaps, including the lack of comprehensive security frameworks and balanced privacy-preserving models in virtual immersive environments. In response, we propose a novel Metaverse Security Architecture designed with Zero-Trust principles. This architecture prioritizes user control over data, identity, and experiences. It also emphasizes proactive security measures to mitigate diverse potential harms in virtual worlds. Our research highlights the critical importance of such robust, user-centric frameworks in enabling a Metaverse that is immersive, safe, and secure for all its participants. 

User Safety and Security in the Metaverse: A Critical Review

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cit2.12220

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