MetaConstructs The Evolution of Digital Twins in the Metaverse Era ( etc.)（Z-Library）

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MetaConstructs

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MetaConstructs The Evolution of Digital Twins in the Metaverse Era Edited by CHIRANJI LAL CHOWDHARY Vellore Institute of Technology, Vellore, India ABHISHEK RANJAN SVKM’s Narsee Monjee Institute of Management Studies (Deemed-to-be University), Mumbai, India JYOTIR MOY CHATTERJEE Department of CSE, Graphic Era University, Dehradun, India PAWAN LINGRAS Saint Mary's University Halifax, NS, Canada

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Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands 125 London Wall, London EC2Y 5AS, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For accessibility purposes, images in electronic versions of this book are accompanied by alt text descriptions provided by Elsevier. For more information, see https://www.elsevier.com/about/ accessibility. Books and Journals published by Elsevier comply with applicable product safety requirements. For any product safety concerns or queries, please contact our authorised representative, Elsevier B.V., at productsafety@elsevier.com. Publisher’s note: Elsevier takes a neutral position with respect to territorial disputes or jurisdictional claims in its published content, including in maps and institutional affiliations. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software. Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-443-40678-2 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Mara Conner Acquisitions Editor: Craig Smith Editorial Project Manager: Sonal Nagpal Production Project Manager: Sathyavani Deivasigamani Cover Designer: Raman Kumar Typeset by MPS Limited, Chennai, India

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Contents List of contributors ix 1. Privacy, security, and ethical considerations 1 Renjith V. Ravi 1.1 Introduction 1 1.2 Privacy and security in cyber-physical systems and digital twins 5 1.3 Threat landscape for cyber-physical systems and digital twins in the Metaverse 9 1.4 Technical solutions for cyber-physical systems and digital twins security 12 1.5 Privacy mechanisms for cyber-physical systems and digital twins in the Metaverse 15 1.6 Digital twins as a key security focus 18 1.7 Future directions and recommendations 20 1.8 Securing the future of cyber-physical systems and digital twins in the Metaverse 23 1.9 Conclusion 25 References 28 2. Artificial intelligence/machine learning for digital twins enabled metaverse 31 Seyedeh Y.H. Mirmahaleh 2.1 Introduction 31 2.2 An overview of the related studies 35 2.3 Digital twins-based application definition 40 2.4 Artificial intelligence and machine learning algorithms for digital twins (artificial intelligence/machine learning) 50 2.5 Supervised machine learning algorithms 50 2.6 Unsupervised machine learning algorithms 60 2.7 Artificial intelligence algorithms 66 2.8 The partial approaches of artificial intelligence and machine learning for digital twins (PAI-PML) 69 2.9 Hardware-based solution for digital twins 71 2.10 Features of the study and its future views 74 2.11 Conclusion 75 References 76 3. Digital twins in healthcare development of an accurate machine learning model for health monitoring through digital twins 79 T. Mariprasath, P. Saraswathi and Sonu Kumar 3.1 Introduction 79 3.2 Case studies of digital twin applications in healthcare 81 v

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3.3 Devices in digital twin scenarios 88 3.4 Application of deep learning 95 3.5 Machine learning algorithm 97 3.6 Conclusion 104 AI disclosure 104 References 104 4. Revolutionizing health care: the role of digital twins in personalized medicine 107 Rakesh Gnanasekaran, Gnanasankaran Natarajan, Sundaravadivazhagan Balasubaramanian and Elakkiya Elango 4.1 Introduction to digital twin health system 107 4.2 Health view system 109 4.3 Convergence of health system 110 4.4 Medical Information 112 4.5 Digital twin system screening EPR screen design and performance (response times) 115 4.6 Conclusion 117 References 117 5. Digital twin and education: a revolutionary pathway to learning 119 Rahul Joshi, Krishna Pandey, Suman Kumari and Rhythma Badola 5.1 Introduction 119 5.2 Understanding the Digital Twin 121 5.3 Metaverse and education 123 5.4 School curriculum & metaverse 125 5.5 Virtual environment of augmented reality 126 5.6 Incorporating digital twin technology into education 127 5.7 Essential elements for developing learner’s digital twin 131 5.8 Optimal strategies for creating and employing digital twins 132 5.9 Digital twin and education in the global world 132 5.10 Challenges in the real world 135 5.11 Conclusion & future scope 136 References 137 6. Bridging real and virtual worlds through digital twins and the metaverse in cultural heritage preservation 141 Udit Mamodiya and Indra Kishor 6.1 Introduction to digital transformation in cultural heritage 141 6.2 Theoretical foundations 142 6.3 Key applications in cultural heritage preservation 152 vi Contents

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6.4 Technological enablers 156 6.5 Challenges and ethical considerations 157 6.6 Future trends 158 6.7 Discussion and implications 160 6.8 Conclusion 161 References 162 Further reading 163 7. Enhancing battery safety through digital twinning in electric vehicles and storage systems 165 Kameswara S.P. Oruganti, Chockalingam A. Vaithilingam, Sridhar S. Indira, Agileswari Ramasamy and Virendra Talele 7.1 Introduction 165 7.2 Approach in the digital twin for battery modeling 169 7.3 Methodology 171 7.4 Results and discussion 173 7.5 Conclusion 178 References 179 8. Convergence of Metaverse and digital twins: evolution, integration, and future challenges in virtual-physical systems 183 Smita Sachin Bhore and Natraj N. A 8.1 Introduction 183 8.2 Basic foundation of Metaverse 184 8.3 Basic foundation of digital twins 193 8.4 Standardization process 195 8.5 Integration of Metaverse and Digital Twins 201 8.6 Challenges, limitations, and future perspective 205 8.7 Conclusion 211 AI Disclosure 212 References 212 9. Quantum computing for digital twins and the metaverse 215 Harpreet Kaur Channi 9.1 Introduction 215 9.2 Literature survey 216 9.3 Methodology 218 9.4 Mathematical formulations and algorithmic implementation 220 9.5 Future roadmap for quantum computing in smart city planning 229 9.6 Results and discussion 229 Contents vii

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9.7 Real world case study 230 9.8 Conclusion 233 References 233 10. Artificial intelligence/machine learning with digital twin enabled metaverse 237 Suneeta Mohanty, Sudhanshu Vaduka and Prasant Kumar Pattnaik 10.1 Introduction 237 10.2 Integration of artificial intelligence/machine learning with digital twin-enabled metaverse 238 10.3 Related work 242 10.4 Challenges 254 10.5 Future directions 255 10.6 Conclusion 255 References 256 11. Enhancing robotic surgery with neuromorphic computing 259 Kanishka Gupta, Amit Aylani, Raju Narzary and Deepak Hajoary 11.1 Introduction 259 11.2 Technical mechanisms of neuromorphic enhancement in surgery 261 11.3 The essence of collaboration between neuromorphic computing and surgical robotics 266 11.4 Neuromorphic platforms relevant to robotic surgery 268 11.5 Case studies and integration examples 272 11.6 Enhancing NeuroArm and neurosurgical robotics 274 11.7 Other emerging systems and research prototypes 277 11.8 Conclusion 278 11.9 Future directions 281 References 284 Index 287 viii Contents

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List of contributors Amit Aylani Vidyalankar Institute of Technology, Mumbai, Maharashtra, India Rhythma Badola Patanjali India Limited, Uttarakhand, India Sundaravadivazhagan Balasubaramanian Department of Information Technology, University of Technology and Applied Sciences, AL Mussanah, Oman Smita Sachin Bhore Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed University), Pune, Maharashtra, India Harpreet Kaur Channi Department of Electrical Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India Elakkiya Elango Department of Computer Science, Government Arts College for Women, Sivaganga, Tamil Nadu, India Rakesh Gnanasekaran Department of Computer Science, Thiagarajar College, Madurai, Tamil Nadu, India Kanishka Gupta Vidyalankar Institute of Technology, Mumbai, Maharashtra, India Deepak Hajoary Bodoland University, Kokrajhar, Assam, India Sridhar S. Indira Solar Research Institute, Universiti Teknologi MARA, Shah Alam, Seleangor, Malaysia Rahul Joshi Department of Journalism & Mass Communication, SMeH, Manav Rachna International Institute of Research & Studies, Haryana, India Indra Kishor Department of Computer Engineering, Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India Sonu Kumar Department of Electronics Engineering, Medi-Caps University, Indore, Madhya Pradesh, India Suman Kumari School of Journalism & Mass Communication, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India Udit Mamodiya ix

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Faculty of Engineering and Technology, Poornima University, Jaipur, Rajasthan, India T. Mariprasath Department of EEE, K.S.R.M College of Engineering (Autonomous), Kadapa, Andhra Pradesh, India Seyedeh Y.H. Mirmahaleh Department of Electrical Engineering, Science and Technology, Lille University, Lille, France Suneeta Mohanty KIIT University, School of Computer Engineering, Bhubaneswar, Odisha, India Natraj N. A Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed University), Pune, Maharashtra, India Raju Narzary Bodoland University, Kokrajhar, Assam, India Gnanasankaran Natarajan Department of Computer Science, Thiagarajar College, Madurai, Tamil Nadu, India Kameswara S.P. Oruganti Fichtner GmbH & Co, Kuala Lumpur, Malaysia Krishna Pandey Department of Journalism & Mass Communication, SMeH, Manav Rachna International Institute of Research & Studies, Haryana, India Prasant Kumar Pattnaik KIIT University, School of Computer Engineering, Bhubaneswar, Odisha, India Agileswari Ramasamy Department of Electrical and Electronics Engineering, Universiti Tenaga Nasional, Malaysia Renjith V. Ravi School of Applied Sciences Engineering and Technology, Rashtriya Raksha University, Gandhinagar, Gujarat, India P. Saraswathi A.P.C. Mahalaxmi College for Women, Thoothukudi, Tamil Nadu, India Virendra Talele Department of Mechanical, Aerospace & Biomedical Engineering, University of Tennessee, Knoxville, TN, United States Sudhanshu Vaduka KIIT University, School of Computer Engineering, Bhubaneswar, Odisha, India Chockalingam A. Vaithilingam CleanTechnology Impact Lab, Taylor's University, Subang Jaya, Malaysia x List of contributors

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CHAPTER 1 Privacy, security, and ethical considerations Renjith V. Ravi School of Applied Sciences Engineering and Technology, Rashtriya Raksha University, Gandhinagar, Gujarat, India 1.1 Introduction With the rapid emergence of technology, highly interconnected and intelligent systems are constantly emerging, where there is no distinction between the physical world and the digital world. One of the advancements is a cyber-physical system (CPS) and digital twin (DT), which are two important technologies to facilitate real-time interactions between the physical and virtual environments [1]. It opens the gate to their integration as a part of the Metaverse, a virtual universe consisting of numerous interconnected digital spaces in the industry of healthcare, manufacturing, smart cities, and so much more. At the same time, this convergence also presents quite challenging privacy and security challenges that must be dealt with one way or the other to create a safe and trustworthy digital physical ecosystem. 1.1.1 Overview of cyber-physical systems and digital twins CPS are defined as components of computing and physical ones that are interconnected and communicate with each other in real time [2]. These systems cipher sensors, actuators, networks, and control mechanisms to link the worlds of the digital and the physical, to facilitate automation, increase efficiencies, and furnish it with alternative decision-making capabilities. CPSs have broad application in various areas such as industrial automation, healthcare, transportation, and energy management. Some examples of CPS are smart grid, autonomous car, and robotic surgical system, in which real-time data processing and precise control are vitally important. Among many other things, CPS is closely related to the idea of DTs, that is, virtual representations of physical objects, processes, and systems that are continuously synchronized with their real-world counterparts [3]. DTs are based upon real-time sensor data, machine learning (ML) algorithms, and artificial intelligence (AI) to simulate, predict, and optimize physical processes. DTs have found a place in manufacturing, aerospace, and urban planning, for example, as companies there are looking to improve efficiency, reduce downtime, and improve predictive maintenance, among other things. Metaconstructs DOI: https://doi.org/10.1016/B978-0-443-40678-2.00001-5 © 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. 1

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The synergy between CPS and DTs promotes natural and data-driven decision- making between the physical and the digital worlds. For example, in the manufacturing sector, DTs of equipment in factories allow for predicting failures and anomalies and optimizing the operations. DTs of patients have the same applications in smart healthcare, where they can be used to simulate treatments and tailor medical interventions. 1.1.2 The emergence of the Metaverse The Metaverse is a quickly developing idea of a continuous, submersed, and interconnected computerized space where clients associate with one another and advanced resources utilizing expanded reality (AR), mixed reality (VR), and blended reality (MR) innovations [4]. Now, there are companies that are at the center of Metaverse development and have invested greatly in AI, Blockchain, immersive technologies, etc. There is Meta (formerly Facebook), Microsoft, Google, and NVIDIA. The Metaverse is the extension of Web 3.0, a decentralized platform with user- centricity and is the next version of the Internet that includes blockchain, decentralized finance, and cryptocurrency [5]. However, unlike the standard digital platforms such as the Internet, a Metaverse intends to create an immersive, perpetual, and compatible virtual reality world where individuals participate in social, professional, and monetary activities. The applications and challenges of the metaverse are depicted in Fig. 1.1. Figure 1.1 Metaverse applications and challenges. 2 Metaconstructs

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Invested in Metaverse, by integrating CPS with DTs, and will have an effect to transform the industry by bringing up hyper reality simulating, improving the remote operation, and enabling the new business models [6]. Some key applications include: • Smart cities: DTs of the entire city help urban planners to optimize infrastructure, monitor factors affecting the environment, and raise efficiency in emergency response systems. • Healthcare: Virtual patient DTs can be used for simulation of accurate medical treatments and personalized treatment plans as well as remote diagnosis. • In manufacturing plants, CPS and DTs are used to optimize production, predictive maintenance, and increase workers' safety. • DTs are used for a virtual shopping experience in the retail and E-commerce sector: With it, customers can have personalized recommendations, augmented reality fitting rooms, and digital product previews. Even though the existence of Metaverse offers tremendous opportunities, increasing dependence of the Metaverse on CPS and DTs brings numerous privacy and security challenges that have to be addressed to assure security, trust, and compliance with the regulation. 1.1.3 Cyber-physical systems and digital twins in the Metaverse: opportunities and risks 1.1.3.1 Opportunities The benefits of this are immense when CPS, DTs, and the Metaverse meet [7]. Some key opportunities include: 1. With the union of CPS and DTs with the Metaverse, organizations can examine an immense extent of real-time information to conquer productivity and respon- siveness. 2. Advanced simulations are immersive and interactive, and they help in optimizing processes, creating better training programs, and better remote collaboration. 3. AI-powered DTs can predict when things may fail, optimize the usage of resources, as well as making operations resilient. 4. CPS integration allows the critical infrastructure being monitored to be operated remotely, thereby reducing not only human error but operational costs. 5. Blockchain-based technology has proven to enable safe transactions, decentralized identity (DID) management, and enhanced digital asset possession inside the Metaverse. Privacy, security, and ethical considerations 3

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1.1.3.2 Risks and challenges Despite these numerous advantages, the integration of CPS and DTs into the Metaverse also entails great challenges, especially along the privacy, security, and ethical channels [8]. Key risks include: 1. Privacy violations and data exploitation: The Meteserver stores a lot of user data, which brings up questions like how personal data is used, provided for, and not allowed to be used. 2. CPS and DTs are cyber threats, including hacking, data breach, ransomware, and denial of service (DoS) attacks. 3. Digital identities in the Metaverse can be misused in identity theft and fraudulent activities, which would affect personal and corporate security. 4. Attackers who maneuver DTs: The perpetrator can manipulate the DTs to introduce chaos in operations, create misinformation, or compromise decision- making processes. 5. The evolving technology of Metaverse and DT technology is a challenge in defining the legal and ethical standards for enforcing in different jurisdictions; hence leading to the evolution of the legal and regulatory uncertainty for the same. 6. These types of mission-critical applications can lead to catastrophic failures, having serious implications on human life in the near future, for example, in the case of autonomous vehicles, healthcare DTs and CPS being compromised. The combination of CPS, DTs, and the Metaverse enables a technological revolution that can revolutionize industry, add an advance of automation, and make better decision-making [9]. Nevertheless, these gains come at the cost of considerable privacy, security, and regulation issues that need to be dealt with in order to prevail in a secure, dependable, and ethical digital environment. In light of industries increasingly onboarding these technologies, it is of utmost importance that cybersecurity frameworks, strong encryption techniques, DID manage- ment, and AI-detected threats are given due importance. Moreover, Metaverse-driven CPS and DT environments must have precise regulations for the protection of the user's privacy, data integrity, and security. 1.1.4 Structure of the paper The privacy and security issues related to CPS and DTs in the Metaverse, which are composed of many facets, are the contents of this article. The subsequent sections are as follows. • In Section 1.2some fundamental privacy and security putative(s) are explored in CPS and DT ecosystems. 4 Metaconstructs

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• Section 1.3 then analyzes the threat landscape primarily in regard to cyber-attacks, data breaches, and identity risks. • Section 1.4 provides technical solutions like cryptography, AI-based security, secure communication protocols, etc. • Section 1.5 provides privacy-preserving mechanisms, such as the identity manage- ment and data protection strategy. • Section 1.6 focuses on DTs and their security implications within CPS environ- ments. • Section 1.7 puts forward future research directions, regulatory framework, and emerging technologies. • Finally, Section 1.8 concludes the paper with key takeaways and gives recommen- dations regarding securing CPS and DTs in the Metaverse. 1.2 Privacy and security in cyber-physical systems and digital twins With CPS and DTs more and more woven into the Metaverse, the Metaverse, as we predicted in 2019, presents some critical problems pertaining to privacy and security [10]. This set of technologies, however, being interconnected, comes with difficulties of data integrity, unauthorized access, and regulatory compliance. These concerns require an understanding and being addressed in order for CPS and DTs to be safely and ethically adopted. 1.2.1 Defining privacy and security in cyber-physical systems and digital twins Privacy is the ability for an individual or an organization to regulate information about themselves with a view to maintaining secrecy [11]. In terms of CPS and DTs, privacy issues include: • Personal identifiable information (PII): CPS and DTs gather an immense amount of personal data in various forms, such as biometric data, geolocation, and behavioral patterns. Access to this data without authorization means there would be severe privacy breaches as well as identity theft. • Corporate and industrial data: CPS is used for manufacturing, automation, and infrastructure management by organizations. Exposure of sensitive operational data can be used for industrial espionage or sabotage, which would affect economic stability and trade secrets. • DTs in the healthcare segment need large amounts of patient data to simulate the medical scenario. The ethical and regulatory concerns of any leakage or misuse of such data arise with laws such as Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). Privacy, security, and ethical considerations 5

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Security, however, targets the steps that may be taken to protect CPS and DTs from cyber threats. Key security mechanisms include: • Data encryption: It encrypts sensitive information so that it cannot be accessed or manipulated by unauthorized persons. • Multifactor authentication and role-based access controls: to prevent unauthorized access to the critical CPS element, thus reducing the vulnerability to cyber-attacks. • AI security analytics: Threat detection and mitigation: AI-driven security analytics monitors continuously for potential threats and mitigates them before they affect the operations of CPS. To reduce the attack risks, ML models are able to detect anomalies as well as unauthorized access attempts. The following table in Table 1.1 compares privacy and security concerns across different CPS applications: 1.2.2 Unique challenges in cyber-physical systems–Metaverse integration Integrating CPS and DTs into the Metaverse brings forth new security ands privacy challenges. [12] Unlike traditional digital systems, CPS must interact in real time between real and digital worlds, with security becoming a complex problem. Below are some key challenges: 1. CPS processes real-time data: It handles a significant amount of real-time data that will come from the sensors. Since this data can be tampered with by any cyberattack, the latter can provide incorrect information to DTs, causing false decisions in industries such as healthcare, finance, and critical infrastructure. 2. Metaverse is built on decentralized data storage systems such as blockchain and peer- to-peer networks. Decentralized storage ensures data integrity, but makes data privacy an issue as the data is dispersed into a number of nodes whose security may vary, as well as the level of regulatory oversight. Table 1.1 Comparison of privacy and security concerns across different cyber-physical systems applications. CPS application Privacy concerns Security concerns Smart cities Surveillance, location tracking Traffic and energy system hacking Healthcare Patient record and genetic data breaches Medical device hacking, ransomware Industrial automation Trade secret exposure, data theft Manufacturing sabotage, system manipulation Autonomous vehicles GPS tracking, data leaks Vehicle control hijacking, signal spoofing 6 Metaconstructs

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3. CPS devices run with different communication protocols. Introducing security vulnerabilities, as secure interoperability across many platforms of the Metaverse does not guarantee security and can enable a man-in-the-middle (MITM) attack targeting the data between systems. 4. Implementing identity management in virtual-physical environments: Avatars and digital identities used in the Metaverse must be associated securely with real people in the real world to avoid fraudulence and abuse. Synthetic identity fraud and deepfake technology render identity management difficult and force mechanisms of authentication to be more crucial than ever. 5. Data protection laws: Works with various local and international data protection laws, such as grouping protocols abiding by GDPR and California Consumer Privacy Act (CCPA) separately. Thus the Metaverse does not have developed regulatory frameworks, which makes it hard to impose privacy and security standards anywhere. Such a lack of standardization creates gaps in compliance and legal uncertainties. All of these challenges were depicted in the following smart diagram in Fig. 1.2 1.2.3 Privacy concerns in digital twin–cyber-physical systems interactions A DT is a virtual replica of a physical system, and so it relies on continuous data synchronization with CPS [13]. This raises several privacy concerns: Figure 1.2 Navigating cyber-physical systems-Metaverse challenges. Privacy, security, and ethical considerations 7

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1.2.3.1 Data collection and ownership • Determining who owns the data generated from interactions between CPS and CPS DT is one of the biggest privacy challenges. For example, who owns the data, if one, in a smart factory, the manufacturer, the software provider, or the end user? • Many organizations find it difficult to create a transparent data collection policy, thus attracting regulatory scrutiny and legal challenges. Ambiguity over data ownership plays a major role in determining how legally accountable and compliant an enterprise is to the privacy laws. 1.2.3.2 Anonymization and data protection • K-anonymity, differential privacy, and homomorphic encryption techniques anonymize the CPS-generated data to prevent unauthorized identification of individuals. However, this approach can still cause the exposure of sensitive data and violation of privacy if incorrectly implemented. • This is prompting the exploration of privacy—enhancing technologies to reduce risks, but there remain challenges between providing privacy and a data utility. Somehow, anonymizing them fully may result in losing the value these data hold for predictive analytics and AI-driven decision-making. 1.2.3.3 Consent and user awareness • Users interact with DTs, often without understanding that they do, as such systems run “in the back end” and collect data without the users' consent. The data collection without the knowledge of the user leads to many ethical and legal issues in sectors like healthcare and finance. • Organizations must use user-friendly consent management platforms, but to provide clear, transparent, and accessible privacy policies. This way, the users receive the information on how their data is used and are given the option to opt out if they wish. 1.2.3.4 Potential for data misuse • Historical and real-time data stored in DTs make them coveted by cybercriminals who might gain access to this data with the intent of using it for espionage, surveillance, or fraud. The misuse in such critical sectors as national defense and financial services can be very costly for the economy and geopolitics. • A necessary protection mechanism is to enforce strict access control policies, robust authentication, and encryption to make sure that unauthorized replication or 8 Metaconstructs

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modification of DTs does not happen. Advanced frameworks for securing them, including zero-trust architectures, may help to mitigate these risks. Since CPS and DTs become essential parts of the Metaverse, it is necessary to mitigate privacy and security risks, including their effects. Besides this, it will be highly important to offer a secure digital ecosystem where robust encryption can ensure data robustness, regulatory compliance must be observed, and ethical data handling practices accepted. To reduce the imminent risks, organizations have to adopt the zero-trust security model, AI-driven threat intelligence, and DID management. Moreover, standard legal frameworks for the handling of CPS and DT data by the Metaverse also have to be defined by policymakers. If the door is left wide open, privacy breaches, cybersecurity threats, as well as ethical issues will continue to grow without clear regulations. The following sections will shed light on the possible cyber threats and their mitigation to security CPS and DTs in the Metaverse. 1.3 Threat landscape for cyber-physical systems and digital twins in the Metaverse However, integration of CPS, and DTs into the Metaverse lead to a number of benefits, they also find themselves exposed to an ever-changing threat landscape [14]. The existing Metaverse has real-time interaction, interlinked digital environments, and can be relied on IoT, AI, and blockchain technologies, thus causing an increase in the potential attack surface of malicious actors [15]. CPS and DTs can be targeted by cyber threats that can corrupt data integrity, affect system availability, hide user privacy, and lower operational safety. Assessing the threat landscape is then critical to developing an appropriate cybersecurity strategy, which will enable the resilience of these technologies. 1.3.1 Cyber attacks on cyber-physical systems in the Metaverse As the Metaverse becomes more embedded with CPS, it makes it more and more attractive to cybercriminals to target it. In such a digital environment, some of the common most cyber threats imposed on CPS are malware, ransomware, Distributed Denial of Service (DDoS) attacks, and access violations. The greatest issue, as far as malware ransomware hits are concerned, is that smart infrastructure, industrial automation, and healthcare systems can be infiltrated by malware and ransomware [16]. Malware is deployed by cybercriminals to disrupt the operation, steal sensitive data, or ransom it in exchange for restoring access. For example, ransomware locking access to a necessary control system in a situated smart factory with interconnected CPS could cause production halts, defective manufacturing processes, or safety hazards, to name a few. Privacy, security, and ethical considerations 9