Metaverse Security and Privacy Research: A Systematic Review
- URL: http://arxiv.org/abs/2507.14985v1
- Date: Sun, 20 Jul 2025 14:42:17 GMT
- Title: Metaverse Security and Privacy Research: A Systematic Review
- Authors: Argianto Rahartomo, Leonel Merino, Mohammad Ghafari,
- Abstract summary: metaverse technologies, including virtual worlds, augmented reality, and lifelogging, have accelerated their adoption across diverse domains.<n>This rise exposes users to significant new security and privacy challenges due to sociotechnical complexity, pervasive connectivity, and extensive user data collection in immersive environments.<n>We present a systematic review of the literature published between 2013 and 2024, offering a comprehensive analysis of how the research community has addressed metaverse-related security and privacy issues over the past decade.
- Score: 0.47109219881156855
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid growth of metaverse technologies, including virtual worlds, augmented reality, and lifelogging, has accelerated their adoption across diverse domains. This rise exposes users to significant new security and privacy challenges due to sociotechnical complexity, pervasive connectivity, and extensive user data collection in immersive environments. We present a systematic review of the literature published between 2013 and 2024, offering a comprehensive analysis of how the research community has addressed metaverse-related security and privacy issues over the past decade. We organize the studies by method, examined the security and privacy properties, immersive components, and evaluation strategies. Our investigation reveals a sharp increase in research activity in the last five years, a strong focus on practical and user-centered approaches, and a predominant use of benchmarking, human experimentation, and qualitative methods. Authentication and unobservability are the most frequently studied properties. However, critical gaps remain in areas such as policy compliance, accessibility, interoperability, and back-end infrastructure security. We emphasize the intertwined technical complexity and human factors of the metaverse and call for integrated, interdisciplinary approaches to securing inclusive and trustworthy immersive environments.
Related papers
- A Survey on Vulnerability Prioritization: Taxonomy, Metrics, and Research Challenges [20.407534993667607]
Resource constraints necessitate effective vulnerability prioritization strategies.<n>This paper introduces a novel taxonomy that categorizes metrics into severity, exploitability, contextual factors, predictive indicators, and aggregation methods.
arXiv Detail & Related papers (2025-02-16T10:33:37Z) - New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook [54.24701201956833]
Security and privacy issues have undermined users' confidence in pre-trained models.
Current literature lacks a clear taxonomy of emerging attacks and defenses for pre-trained models.
This taxonomy categorizes attacks and defenses into No-Change, Input-Change, and Model-Change approaches.
arXiv Detail & Related papers (2024-11-12T10:15:33Z) - A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
stance detection has emerged as a crucial subfield within affective computing.
Recent years have seen a surge of research interest in developing effective stance detection methods.
This paper provides a comprehensive survey of stance detection techniques on social media.
arXiv Detail & Related papers (2024-09-24T03:06:25Z) - Centering Policy and Practice: Research Gaps around Usable Differential Privacy [12.340264479496375]
We argue that while differential privacy is a clean formulation in theory, it poses significant challenges in practice.
To bridge the gaps between differential privacy's promises and its real-world usability, researchers and practitioners must work together.
arXiv Detail & Related papers (2024-06-17T21:32:30Z) - Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives [48.48294460952039]
This survey offers comprehensive descriptions of the privacy, security, and fairness issues in federated learning.
We contend that there exists a trade-off between privacy and fairness and between security and sharing.
arXiv Detail & Related papers (2024-06-16T10:31:45Z) - Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectives [10.16399860867284]
We present a comprehensive survey of the metaverse, envisioned as a transformative dimension of next-generation Internet technologies.
We analyze its architecture by defining key characteristics and requirements, thereby illuminating the nascent reality set to revolutionize digital interactions.
We extend our scrutiny to critical technologies integral to the metaverse, including interactive experiences, communication technologies, ubiquitous computing, digital twins, artificial intelligence, and cybersecurity measures.
arXiv Detail & Related papers (2024-05-07T23:49:02Z) - Object Detectors in the Open Environment: Challenges, Solutions, and Outlook [95.3317059617271]
The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors.
This paper aims to conduct a comprehensive review and analysis of object detectors in open environments.
We propose a framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes.
arXiv Detail & Related papers (2024-03-24T19:32:39Z) - A Narrative Review of Identity, Data, and Location Privacy Techniques in Edge Computing and Mobile Crowdsourcing [2.5944208050492183]
This review focuses on the need for privacy protection in mobile crowdsourcing and edge computing.
We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy.
This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers.
arXiv Detail & Related papers (2024-01-20T19:32:56Z) - A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing.
Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data.
Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
arXiv Detail & Related papers (2023-09-27T14:38:16Z) - Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and
Opportunities [68.03971716740823]
In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users.
This survey focuses on the representation and intelligence for the four fundamental system components in ubiquitous Metaverse.
arXiv Detail & Related papers (2023-07-13T11:14:46Z) - A research infrastructure for generating and sharing diversity-aware
data [0.0]
Data flow associated with trend of computerizing aspects of people's diversity in their daily lives is associated with issues concerning people protection and their trust in new technologies.
We argue for the development of an end-to-end research infrastructure that enables trustworthy diversity-aware data within a citizen science community.
arXiv Detail & Related papers (2023-06-16T10:43:42Z) - Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges [29.22630037716171]
We conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse.
We first introduce underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse.
Next, we review and summarize state-the-art solutions based on learning, differential privacy, homomorphic encryption, and zero-knowledge for different privacy problems in metaverse.
arXiv Detail & Related papers (2023-04-23T13:05:58Z) - Towards Safer Generative Language Models: A Survey on Safety Risks,
Evaluations, and Improvements [76.80453043969209]
This survey presents a framework for safety research pertaining to large models.
We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models.
We explore the strategies for enhancing large model safety from training to deployment.
arXiv Detail & Related papers (2023-02-18T09:32:55Z) - New Challenges in Reinforcement Learning: A Survey of Security and
Privacy [26.706957408693363]
Reinforcement learning (RL) is one of the most important branches of AI.
RL has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics.
Some of these applications and systems have been shown to be vulnerable to security or privacy attacks.
arXiv Detail & Related papers (2022-12-31T12:30:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.