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Ensuring Privacy in Digital Twin Ecosystems

Edited by Shubham Mahajan, Davinder Paul Singh, Amit Kant Pandit, and Paras Chawla
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394383719  |  Hardcover  |  
562 pages
Price: $225 USD
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One Line Description
Digital Twin Privacy; Data Security in Digital Twins; Privacy-by-Design; Digital Twin Ecosystems; AI-driven Privacy Solutions; Data Anonymization Techniques; Blockchain for Privacy; Digital Twin Security Frameworks; Healthcare Digital Twins; Smart City Privacy; Industrial Digital Twins; GDPR Compliance in Digital Twins; Secure Digital Twins; IoT and Digital Twin Privacy; Quantum Computing and Privacy

Description
Digital twins, which create virtual replicas of physical entities, enable real-time monitoring, predictive analytics, and optimized operations, revolutionizing the way organizations approach efficiency, decision-making, and innovation. However, with the growing reliance on digital twins comes an escalating concern about privacy and data security. These ecosystems collect vast amounts of sensitive data, making them prime targets for cyber threats and privacy breaches. This book focuses on the intricate challenges and solutions related to maintaining privacy and data security in digital twin environments. It provides comprehensive insights into the security frameworks, technologies, and best practices that ensure the protection of digital twin environments, safeguarding them from breaches, misuse, and unauthorized access. The content will delve into a variety of topics, including privacy-by-design strategies, encryption techniques, data anonymization methods, and AI-driven privacy solutions specifically tailored for digital twin ecosystems. Case studies will be explored to highlight real-world examples of privacy vulnerabilities and how organizations overcame these challenges. By integrating technical discussions with legal and ethical frameworks, the book offers a holistic approach that caters to both practitioners and researchers at an intermediate-to-advanced level.

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Author / Editor Details
Shubham Mahajan, PhD is a Visiting Assistant Professor in the Department of Communication and Computer Engineering at Al-Ahliyya Amman University in Jordan. He has published more than 70 articles in peer-reviewed journals and conferences, as well as 11 Indian, one Australian, and one German patents. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication.

Davinder Paul Singh, PhD is an Assistant Professor in the Department of Computer Science and Engineering at Pandit Deendayal Energy University, Gandhinagar, Gujarat. He has published various articles in SCI journals and international conferences of repute. His primary areas of interest include artificial intelligence, machine learning, deep learning, computational biology, and drug discovery.

Amit Kant Pandit, PhD is a Professor in the Department of Electronics and Computer Engineering at Shri Mata Vaishno Devi University, Katra, India with more than 26 years of experience. He has authored and co-authored more than 80 publications, including research papers in peer-reviewed journals and conferences, and two Indian and one Australian patent. He specializes in artificial intelligence and image processing.

Paras Chawla, PhD is a distinguished academic leader, researcher, and innovator with more than 24 years of experience spanning academia, research, industry, and administration. He serves as a Professor and Director in the Amity School of Engineering and Technology at the Amity Institute of Information Technology. He has published more than 120 research papers and holds 24 patents, 15 of which have been granted. His expertise is in AI, machine learning, data science, 5G communications, computer vision, and smart technologies.

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Table of Contents
Preface
Acknowledgement
1. Balancing Innovation and Privacy: Navigating the Landscape of Digital Twin Ecosystems
Manish Kumar Panday, Manish Baboo Agarwal, Manu Mehrotra and Seema Agarwal
1.1 Overview of Digital Twin Ecosystems (DTEs)
1.1.1 Information Security Challenges in IoT
1.2 Developing Digital Twins Further
1.2.1 Structure and Functionality
1.2.2 Real-Time Data Analysis and Optimization
1.2.3 Key Application Across Various Sectors
1.3 Privacy Concerns Related to Digital Twins Ecosystem
1.3.1 Gaps in Surveillance and Data Collection
1.3.2 Integration Complexities in Smart Cities
1.3.3 Exacerbation of Privacy Risks
1.4 Benefits of Decentralization
1.5 Encryption Techniques and Data Security
1.6 Compliance with Governance and Other Policies
1.6.1 Policy Changes and Developments in Data Protection Regulations
1.6.2 Shortcomings in the Current Legal Structures
1.6.3 Strategic and Tactical Gaps in Setting Expectations
1.7 Approaches to Shifting to Proactively Protective Measures for Digital Privacy
1.7.1 Implementation of Strict Access Controls
1.7.2 Regular Audit and Risk Assessment
1.7.3 Implementation of Advanced Methodologies to Resolve Privacy Concerns
1.8 Conclusion
1.8.1 Future Directions for Evolving Secure Digital Twin Ecosystems
1.8.2 Striking a Balance between Innovative Developments and Privacy Protection
1.8.3 Call for Collaborations Concerning Policy, Technology, and Other Domains
References
2. Digital Twins and Privacy: Navigating the Nexus of Innovation and Data Protection
Ashima Kalra, Bhawna Tandon and Panchal Sandeep Govindrao
2.1 Introduction
2.1.1 What are Digital Twins?
2.1.2 The Rise of Data-Driven Ecosystems
2.1.3 Privacy in Digital Twin Systems
2.2 Digital Twins: Foundations and Use Cases
2.2.1 Definition and Core Components
2.2.2 Types of Digital Twins
2.2.3 Applications Across Industries
2.3 Privacy Threats in Digital Twin Environments
2.3.1 Data Collection and Surveillance Risks
2.3.2 Re-Identification and Profiling
2.3.3 Data Fusion and Inference Attacks
2.3.4 Insider Threats and External Breaches
2.4 Legal and Regulatory Landscape
2.4.1 GDPR, HIPAA, and Other Frameworks
2.4.2 Compliance Challenges for Digital Twin Developers
2.4.3 Data Ownership and Consent in Virtual Representations
2.4.4 Cross-Border Data Transfer Issues
2.5 Privacy-Preserving Techniques for Digital Twins
2.5.1 Federated Learning and Edge AI
2.5.2 Differential Privacy (DP)
2.5.3 Homomorphic Encryption (HE) and Secure Multiparty Computation (SMPC)
2.5.4 Privacy-by-Design Principles in Twin Architectures
2.6 Ethical and Societal Considerations
2.6.1 Transparency and Trust
2.6.2 Autonomy and Human Digital Representation
2.6.3 The Digital Divide and Surveillance Capitalism
2.7 Case Studies and Real-World Challenges
2.7.1 Smart City Digital Twins and Resident Data
2.7.2 Healthcare Twins and Sensitive Health Records
2.7.3 Industrial IoT Twins and Employee Monitoring
2.8 Future Directions and Open Research Problems in Privacy-Preserving Digital Twin Systems
2.8.1 Privacy Metrics for Digital Twin Systems
2.8.2 Adaptive Privacy Controls
2.8.3 Synthetic Data Generation
2.8.4 Standardization and Interoperability
2.9 Conclusion
2.9.1 Summary of Key Points
2.9.2 Balancing Innovation with Responsibility
2.9.3 Toward Trustworthy Digital Twins
Bibliography
3. Privacy in Smart City Digital Twin
Lingaraj K., S. Supreeth, Rashmi Laxmikant M., Karthik Rao M. C., Lalit Garg and D. Viswanath Reddy
3.1 Introduction
3.2 Understanding Smart City Digital Twins
3.2.1 Definition and Components
3.2.2 Components of a Smart City Digital Twin
3.2.3 Data Sources and Integration
3.2.4 Use Cases
3.3 Privacy Challenges in Smart City Digital Twins
3.4 Privacy by Design in SCDT Architectures
3.4.1 Principles: Privacy by Design in SCDT Architectures
3.4.2 Privacy-Preserving Technologies in SCDT Architectures
3.4.3 Layered Architecture for Privacy in Smart City Digital Twin (SCDT)
3.5 Legal and Ethical Frameworks
3.5.1 Regulatory Landscape
3.5.2 Privacy Impact Assessments (PIA)
3.5.3 Ethics Guidelines
3.6 Case Studies
3.7 Future Directions
Conclusion
References
4. Future Trends and Emerging Challenges in Digital Twins
Kavita Arora, Neha Gupta and Wanqing Tu
Introduction
Overview of Digital Twins
Digital Twins Necessitate the Following Components
Digital Twins Design Goals
Evolution of Digital Twin Technology
Current State of Digital Twins
Key Industries Using Digital Twins
Emerging Trends in Digital Twin Technology
Digital Twins for Sustainability and Environmental Impact
Synergy between Digital Twins and Sustainability
Use Cases and Future Applications of Digital Twins
The Role of Digital Twins in Decision-Making
The Impact of Quantum Computing on Digital Twin Models
Quantum Computing’s Function in Digital Twins
Quantum Computing’s Significant Contributions to Digital Twins
Real-World Applications of Quantum-Enhanced Digital Twins
Identifying Challenges and Limitations in Digital Twin Implementation
Vision for the Future of Digital Twins
References
5. The Future of Privacy in Digital Twin Ecosystems
Anandkumar Pardeshi and Sujata Deshmukh
5.1 Introduction
5.1.1 Definition and Significance of Digital Twin Ecosystems
5.1.2 Privacy Challenges in Interconnected Digital Twin Environments
5.1.3 Objectives and Structure of the Chapter
5.2 Privacy Threats in Digital Twin Ecosystems
5.2.1 Data Integrity and Security Risks
5.2.2 Identity and Anonymity Concerns
5.3 Adaptive Privacy-Preserving Mechanisms in Digital Twin Ecosystems
5.3.1 Federated Learning for Secure Data Collaboration
5.3.2 Blockchain for Authentication and Tamper-Proof Verification
5.4 Decentralized Identity and User-Controlled Privacy in Digital Twin Ecosystems
5.4.1 Self-Sovereign Identity (SSI) Frameworks
5.4.2 Zero-Knowledge Proofs and Privacy-Preserving Authentication
5.5 Next-Generation Technologies and Privacy Implications in Digital Twin Ecosystems
5.5.1 6G Networks and Cyber-Resilient Digital Twins
5.5.2 Quantum-Resistant Cryptography
5.5.3 AI-Driven Behavioral Analytics
5.6 Regulatory and Ethical Considerations in Digital Twin Ecosystems
5.6.1 Compliance with Evolving Data Protection Laws
5.6.2 Ethical Challenges in AI-Powered Digital Twins
5.7 Case Studies and Industry Applications of Privacy-Preserving Digital Twins
5.7.1 Healthcare Digital Twins
5.7.2 Smart Cities and Urban Metaverse Twins
5.7.3 Edu-Metaverse and Learning Ecosystems
5.8 Future Research Directions in Digital Twin Privacy
5.8.1 Open Challenges in Privacy-Preserving Digital Twins
5.8.2 Potential Advancements
5.9 Conclusion
5.9.1 Summary of Key Findings
5.9.2 Final Recommendations for Future Digital Twin Ecosystems
5.9.3 Concluding Remarks
References
6. The Future of Privacy in Digital Twin Ecosystems: Risks, Technologies, and Regulations
Sandhya Soman, Suresh K., Gnanasankaran Natarajan and Sundaravadivazhagan Balasubramaniam
6.1 Introduction
6.2 Digital Twin Ecosystem: An Overview
6.2.1 Components of a Digital Twin
6.2.2 Applications of Digital Twins
6.2.3 Data Flow in the Digital Twin Ecosystem
6.3 Privacy Challenges in the Digital Twin Ecosystems
6.3.1 Data Collection and Storage Risks
6.3.2 Data Sharing and Interoperability Issues
6.3.3 Cybersecurity Threats and Data Breaches
6.3.4 Ethical and Legal Considerations
6.3.5 Balancing Privacy Risks with Data-Driven Collaboration
6.4 The Future of Privacy in Digital Twin Ecosystems
6.4.1 Federated Learning (FL) and Privacy-Preserving AI
6.4.2 Blockchain for Secure Data Management
6.4.3 Homomorphic Encryption and Differential Privacy
6.4.4 Zero-Knowledge Proofs (ZKPs) for Data Anonymisation
6.4.5 Decentralised Identity Management and Self-Sovereign Identity
6.4.6 Emerging Privacy-Preserving Technologies
6.5 Future Trends and Predictions
6.5.1 Privacy by Design (PbD) in the DTE Development
6.5.2 AI-Driven Automated Privacy Enforcement
6.5.3 Potential Impact of Quantum Computing on Privacy
6.6 Conclusion and Research Directions
6.6.1 Summary of Key Insights
6.6.2 Privacy as a Pillar of Trust
6.6.3 Need for “Privacy by Design” and Interdisciplinary Collaboration
6.6.4 Open Research Problems
6.6.5 Recommendations
6.6.6 Privacy Implementation Guidelines for Enterprises Using DTEs
References
7. Privacy in the Age of Digital Twins
Shobha Chawla, Neha Gupta, Vijayalaxmi Rajendran and Akhilesh Tiwari
7.1 Introduction
7.2 Security Threats to Digital Twins
7.3 Design of Data Privacy Law
7.4 Strategies for Securing Digital Twins
7.5 Privacy by Design
Conclusion
References
8. AI and ML for Privacy-Preserving Digital Twins: Challenges and Opportunities
Raj Kishor Verma, Gaurav Agarwal and Nitin Pandey
8.1 Introduction
8.1.1 Digital Twins
8.1.2 Privacy
8.1.3 Artificial Intelligence (AI)
8.1.4 Machine Learning (ML)
8.1.5 Privacy Enhancement
8.1.6 Data Security
8.2 Literature Review
8.3 Proposed Worked
8.3.1 Mathematical Formula of AI and ML for Privacy-Preserving Digital Twins: Challenges and Opportunities
8.3.2 Algorithm: Privacy-Preserving Machine Learning for Digital Twins (Using Differential Privacy)
8.4 Conclusion
8.5 Future and Scope
8.6 Opportunities and Challenges
References
9. Federated Explainable Hybrid Learning Framework (FEHLF) for Real-Time, Resource-Constrained Big Data Applications
Tamanna Sharma, Neha Arya, Chirag Sharma and Sonam Nagpal
Introduction
Literature Study
Security-Centric Big Data Solutions
Graph-Based and AI-Integrated Big Data Systems
Big Data in Health Care and Biomedical Domains
Analytics-Driven Approaches: Social, Financial, Urban
Emerging Domains and Cross-Disciplinary Applications
Application Scenarios
Research Gaps and Objectives
Proposed Approach
Results and Discussion
Conclusion and Future Scope
References
10. Revolutionizing Healthcare: Digital Twins and the Role of Cyber Physical Systems in Modern Medicine
Krishna Pandey, Suman Kumari, Rhythma Badola and Rahul Joshi
10.1 Introduction
10.2 Cyber-Physical System
10.3 Evolution of Cloud Security Enabled DT
10.4 Effective Competence in Hospital Using DT
10.4.1 Healthcare Equipment and DT
10.5 DT in Device Development
10.5.1 Optimizing Resources
10.5.2 Risk Management
10.5.3 Personalized Diagnosis
10.5.4 Drugs and Their Development
10.6 Medical Devices, Medical Testing and Treatments, and Medical Wearables
10.7 Regulatory Services
10.8 DT Frameworks in Healthcare
10.8.1 Forecasting Phase
10.8.2 Observing Phase
10.8.3 Comparison Phase
10.9 Cyber Flexibility in Healthcare and DT
10.10 CPS and DT
10.10.1 Mapping in CPS and DT
10.11 Advantages and Limitations of DT
10.12 Conclusion and Future Scope
References
11. Safeguarding Privacy and Security in AI-Powered Healthcare Systems
Arpita Maheriya and Sailesh Suryanarayan Iyer
11.1 Introduction
11.2 Background Study
11.3 Challenges in Building Secure AI-Based Healthcare Data System
11.4 Frameworks, Acts, and Policies for Data Privacy Protection
11.5 Attacks on AI Healthcare Model
11.6 Privacy-Preserving Techniques
11.6.1 Cryptographic Algorithms
11.6.1.1 Homomorphic Encryption
11.6.1.2 Secure Multiparty Communication
11.6.1.3 Garbled Circuits
11.6.1.4 Secret Sharing
11.6.2 Noncryptographic Techniques
11.6.2.1 Differential Privacy
11.6.2.2 Data Anonymization
11.6.2.3 Synthetic Data Generation
11.6.3 Federated Learning
11.6.4 Blockchain
11.7 Quantum Healthcare Data
11.8 Future Research Directions
11.9 Conclusion
References
12. Neuro-Symbolic AI for Cardiovascular Pharmacovigilance: A Holistic Approach to Drug-Induced Cardiotoxicity
Jossy P. George, Kamal Upreti and Bosco Paul Alapatt
12.1 Introduction
12.2 Overview of Cardiovascular Drug Safety Concerns
12.3 The Clinical Burden of Drug-Induced Cardiotoxicity
12.4 Challenges with Traditional PV Systems
12.5 Motivation for AI Integration—Need for Explainability and Clinical Trust
12.6 Background and Theoretical Foundations
12.6.1 Cardiotoxicity: Definitions and Types
12.6.2 PV Frameworks
12.6.3 AI in Drug Safety Monitoring
12.6.4 Introduction to Neuro-Symbolic AI
12.7 Future Directions
12.8 Conclusion
References
13. Cloud Computing in Education: Challenges, Limitations and Future Prospect
Arun Kumar Rana, Mandeep Kaur, Sumit Kumar Rana, Deepak Kumar and Maksud Alam
13.1 Introduction
13.2 What is Cloud Computing?
13.3 The Role of Cloud Computing in Education
13.3.1 Online Learning Platforms
13.3.2 Data Storage and Management
13.3.3 Collaborative Tools
13.3.4 Virtual Classrooms
13.3.5 Administrative Management
13.3.6 Cost Efficiency
13.4 Benefits of Cloud Computing in Education
13.4.1 Accessibility and Flexibility
13.4.2 Enhanced Collaboration
13.4.3 Scalability
13.4.4 Cost Savings
13.4.5 Data Security
13.4.6 Easy Integration of New Technologies
13.5 Challenges and Limitations
13.5.1 Privacy and Data Security
13.5.2 Internet Access
13.5.3 Vendor Lock-In
13.5.4 Technical Support and Training
13.6 Future Prospects of Cloud Computing in Education
13.7 Conclusion
References
14. Cloud Computing Security: Challenges and Solutions with Emerging Trends and Future Directions
Arun Kumar Rana, Nada Ratkovic, Kamalakanta Muduli, Sumit Kumar Rana, Deepak Kumar and Mohit Mittal
14.1 Introduction
14.2 Cloud Computing Models and Architectures
14.3 Security Challenges in Cloud Computing
14.3.1 Data Security and Privacy
14.3.2 Data Loss and Availability
14.3.3 Shared Responsibility Model
14.3.4 Insider Threats
14.3.5 Identity and Access Management
14.3.6 Compliance and Legal Issues
14.4 Security Solutions and Best Practices
14.4.1 Data Encryption
14.4.2 Secure APIs
14.4.3 Cloud Security Devices
14.4.4 Security Observing and Inspecting
14.4.5 Zero-Trust Design
14.4.6 Multicloud and Hybrid Security Strategies
14.5 Emerging Trends and Future Directions
14.5.1 Fake Insights (AI) and ML
14.5.2 Blockchain for Cloud Security
14.5.3 Quantum Computing and Cryptography
14.6 Conclusion
References
15. Author Royalty Distribution System Using Blockchain Technology
Konki Mohit and R. Kanniga Devi
15.1 Introduction
15.1.1 Background
15.1.2 Objective
15.1.3 Problem Statement
15.1.4 Existing System
15.1.5 Scope of the Project
15.2 Literature Review
15.2.1 Related Works
15.2.1.1 Real-World Implementations of Blockchain in Royalty Management
15.2.2 Overview of Blockchain Technology
15.2.3 Smart Contracts and Royalty Distribution
15.2.4 Transparency and Trust in Royalty Systems
15.2.5 Research Gaps
15.3 Proposed System
15.3.1 System Architecture
15.3.1.1 User Interface Layer (Front-End)
15.3.1.2 Blockchain Layer (Smart Contracts)
15.3.1.3 Sales Data Layer—Off-Chain Storage
15.3.1.4 Back Layer (API and Database)
15.3.1.5 Payment Layer (Blockchain Wallets and Cryptocurrency Payments)
15.3.1.6 Security Layer
15.3.1.7 Monitoring and Analytics Layer
15.3.2 Key Features and Advantages
15.3.3 Expected Outcomes
15.4 Methodology
15.4.1 Blockchain Development
15.4.1.1 Platform Selection
15.4.1.2 Smart Contract Design
15.4.1.3 Testing Environment
15.4.2 Decentralized Application Development
15.4.2.1 Front-End
15.4.2.2 Blockchain Integration
15.4.3 Smart Contract Execution and Security
15.4.4 Back-End API Development
15.4.5 Testing and Security Protocols
15.5 Models
15.5.1 Author Model
15.5.2 Content Model
15.5.3 Smart Contract Model
15.5.4 Sales Model
15.5.5 User Interaction Model
15.6 Results and Discussion
15.6.1 System Performance
15.6.2 User Feedback and Interaction
15.6.2.1 Benefits to Authors
15.6.3 System Demonstration
15.6.4 Analysis of Key Findings
15.7 Conclusion and Future Work
15.7.1 Summary of Findings
15.7.2 Future Scope
References
16. LCR Circuit Analysis of Incident Angle Metamaterial Absorber for Double-Band Wi-Fi Applications in IoT-Based Sensors: A Mathematical Insight
Ram Krishna, Harendra Singh, Arun Kumar Rana, Anurag Singh and Avinash Kumar
16.1 Introduction
16.2 Generalized Model of Transmission Line for the Nonseparated Absorber
16.3 Suggested Double-Band Absorber Circuit Model
16.4 Estimated Value of Resistance of Each LCR by Equivalent Circuit Analysis
16.5 Computation of the Absorber’s Size
16.6 Discussion
16.7 Conclusion
Bibliography
17. Digital Twin Technology in Education: Applications, Benefits, Challenges, Ethical Issues and Security Concerns
Ankur Nandi, Milan Barman, Sourav Sen, Tapash Das and Tarini Halder
17.1 Introduction
17.2 Objectives
17.3 Methodology
17.4 Discussion
17.4.1 Applications of DT Technology in the Education Sector
17.4.2 Benefits of Using DT Technology in Education
17.4.3 Challenges of Using DT Technology in Education
17.4.4 Ethical and Security Concerns Associated with Using DT Technology
17.4.4.1 Ethical Concerns Associated with Using DT Technology
17.4.4.2 Security Concerns Associated with Using DT Technology
17.5 Conclusion
Bibliography
18. Navigating the Future: Cloud Computing’s Progress and Challenges in Healthcare
Arun Kumar Rana, Sumit Kumar Rana, Deepak Kumar, Ram Krishna, Anurag Singh and Harendra Singh
Introduction
18.1 What is Cloud Computing?
18.2 Advantages of Cloud Computing in Healthcare
18.2.1 Scalability
18.2.2 Cost Efficiency
18.2.3 Improved Collaboration
18.2.4 Data Accessibility
18.2.5 Resource Optimization
18.2.6 Automated Workflows
18.2.7 Disaster Recovery and Business Continuity
18.3 Challenges of Cloud Computing in Healthcare
18.3.1 Security and Privacy
18.3.2 Data Interoperability
18.3.3 Regulatory Compliance
18.3.4 Reliability and Downtime
18.3.5 Blockchain Integration
18.4 Cloud Computing Models in Healthcare
18.4.1 Public Cloud
18.4.2 Private Cloud
18.4.3 Hybrid Cloud
18.5 Use Cases of Cloud Computing in Healthcare
18.5.1 EHR Management
18.5.2 Telemedicine
18.5.3 Medical Imaging
18.5.4 Big Data Analytics
18.5.5 Drug Discovery and Personalized Medicine
18.5.6 Operational Efficiency
18.5.7 Remote Patient Monitoring
18.5.8 Telemedicine and Surgical Robotics
18.5.9 Digital Healthcare Platforms and Collaboration
18.6 Future Trends and Opportunities
18.6.1 AI and ML Integration
18.6.2 Edge Computing
18.6.3 Blockchain for Healthcare Data Security
18.6.4 Personalized and Precision Medicine
18.6.5 Global Health and Accessibility
18.6.6 Internet of Medical Things Integration
Conclusion
References
Index

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