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Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey (2310.10665v1)

Published 19 Sep 2023 in cs.CR, cs.AI, and cs.LG

Abstract: The metaverse is a nascent concept that envisions a virtual universe, a collaborative space where individuals can interact, create, and participate in a wide range of activities. Privacy in the metaverse is a critical concern as the concept evolves and immersive virtual experiences become more prevalent. The metaverse privacy problem refers to the challenges and concerns surrounding the privacy of personal information and data within Virtual Reality (VR) environments as the concept of a shared VR space becomes more accessible. Metaverse will harness advancements from various technologies such as AI, Extended Reality (XR), Mixed Reality (MR), and 5G/6G-based communication to provide personalized and immersive services to its users. Moreover, to enable more personalized experiences, the metaverse relies on the collection of fine-grained user data that leads to various privacy issues. Therefore, before the potential of the metaverse can be fully realized, privacy concerns related to personal information and data within VR environments must be addressed. This includes safeguarding users' control over their data, ensuring the security of their personal information, and protecting in-world actions and interactions from unauthorized sharing. In this paper, we explore various privacy challenges that future metaverses are expected to face, given their reliance on AI for tracking users, creating XR and MR experiences, and facilitating interactions. Moreover, we thoroughly analyze technical solutions such as differential privacy, Homomorphic Encryption (HE), and Federated Learning (FL) and discuss related sociotechnical issues regarding privacy.

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References (169)
  1. S. Mystakidis, “Metaverse,” Encyclopedia, vol. 2, no. 1, pp. 486–497, 2022.
  2. ——, “Metaverse,” Encyclopedia, vol. 2, no. 1, pp. 486–497, 2022. [Online]. Available: https://www.mdpi.com/2673-8392/2/1/31
  3. C. Flavián, S. Ibáñez-Sánchez, and C. Orús, “The impact of virtual, augmented and mixed reality technologies on the customer experience,” Journal of Business Research, vol. 100, pp. 547–560, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0148296318305319
  4. R. Zhao, Y. Zhang, Y. Zhu, R. Lan, and Z. Hua, “Metaverse: Security and privacy concerns,” arXiv preprint arXiv:2203.03854, 2022.
  5. Y. Wang, Z. Su, N. Zhang, R. Xing, D. Liu, T. H. Luan, and X. Shen, “A survey on metaverse: Fundamentals, security, and privacy,” IEEE Communications Surveys & Tutorials, 2022.
  6. Y. Huang, Y. J. Li, and Z. Cai, “Security and privacy in metaverse: A comprehensive survey,” Big Data Mining and Analytics, vol. 6, no. 2, pp. 234–247, 2023.
  7. B. Falchuk, S. Loeb, and R. Neff, “The social metaverse: Battle for privacy,” IEEE Technology and Society Magazine, vol. 37, no. 2, pp. 52–61, 2018.
  8. H. Ning, H. Wang, Y. Lin, W. Wang, S. Dhelim, F. Farha, J. Ding, and M. Daneshmand, “A survey on metaverse: the state-of-the-art, technologies, applications, and challenges,” arXiv preprint arXiv:2111.09673, 2021.
  9. Q. Yang, Y. Zhao, H. Huang, Z. Xiong, J. Kang, and Z. Zheng, “Fusing blockchain and ai with metaverse: A survey,” IEEE Open Journal of the Computer Society, vol. 3, pp. 122–136, 2022.
  10. M. Xu, W. C. Ng, W. Y. B. Lim, J. Kang, Z. Xiong, D. Niyato, Q. Yang, X. S. Shen, and C. Miao, “A full dive into realizing the edge-enabled metaverse: Visions, enabling technologies, and challenges,” IEEE Communications Surveys & Tutorials, 2022.
  11. T. Huynh-The, Q.-V. Pham, X.-Q. Pham, T. T. Nguyen, Z. Han, and D.-S. Kim, “Artificial intelligence for the metaverse: A survey,” Engineering Applications of Artificial Intelligence, vol. 117, p. 105581, 2023.
  12. S.-M. Park and Y.-G. Kim, “A metaverse: taxonomy, components, applications, and open challenges,” IEEE access, vol. 10, pp. 4209–4251, 2022.
  13. J. Wang, H. Du, X. Yang, D. Niyato, J. Kang, and S. Mao, “Wireless sensing data collection and processing for metaverse avatar construction,” arXiv preprint arXiv:2211.12720, 2022.
  14. E. D. Peterson, “Machine learning, predictive analytics, and clinical practice: can the past inform the present?” Jama, vol. 322, no. 23, pp. 2283–2284, 2019.
  15. S. Erisen, “Real-time learning and monitoring system in fighting against sars-cov-2 in a private indoor environment,” 2022.
  16. P. Zhang, R.-P. Chen, and H.-N. Wu, “Real-time analysis and regulation of epb shield steering using random forest,” Automation in Construction, vol. 106, p. 102860, 2019.
  17. E. X. Jiao and J. L. Chen, “Tourism forecasting: A review of methodological developments over the last decade,” Tourism Economics, vol. 25, no. 3, pp. 469–492, 2019.
  18. T. Morimoto, T. Kobayashi, H. Hirata, K. Otani, M. Sugimoto, M. Tsukamoto, T. Yoshihara, M. Ueno, and M. Mawatari, “Xr (extended reality: virtual reality, augmented reality, mixed reality) technology in spine medicine: status quo and quo vadis,” Journal of Clinical Medicine, vol. 11, no. 2, p. 470, 2022.
  19. S. H.-W. Chuah, “Why and who will adopt extended reality technology? literature review, synthesis, and future research agenda,” Literature Review, Synthesis, and Future Research Agenda (December 13, 2018), 2018.
  20. S. Doolani, C. Wessels, V. Kanal, C. Sevastopoulos, A. Jaiswal, H. Nambiappan, and F. Makedon, “A review of extended reality (xr) technologies for manufacturing training,” Technologies, vol. 8, no. 4, p. 77, 2020.
  21. A. Morris, J. Guan, N. Lessio, and Y. Shao, “Toward mixed reality hybrid objects with IoT avatar agents,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, pp. 766–773.
  22. K. D. Setiawan, A. Anthony, Meyliana, and Surjandy, “The essential factor of metaverse for business based on 7 layers of metaverse – systematic literature review,” in 2022 International Conference on Information Management and Technology (ICIMTech), 2022, pp. 687–692.
  23. T. Li, C. Yang, Q. Yang, S. Zhou, H. Huang, and Z. Zheng, “Metaopera: A cross-metaverse interoperability protocol,” 2023.
  24. R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill et al., “On the opportunities and risks of foundation models,” arXiv preprint arXiv:2108.07258, 2021.
  25. M. Firat, “How Chat GPT can transform autodidactic experiences and open education?” Jan 2023. [Online]. Available: osf.io/9ge8m
  26. Z. Shi, N. Luktarhan, Y. Song, and G. Tian, “Bfcn: A novel classification method of encrypted traffic based on bert and cnn,” Electronics, vol. 12, no. 3, p. 516, Jan 2023. [Online]. Available: http://dx.doi.org/10.3390/electronics12030516
  27. P. Delobelle, T. Winters, and B. Berendt, “Robbert: a dutch roberta-based language model,” arXiv preprint arXiv:2001.06286, 2020.
  28. H. Wang, X. Hu, and H. Zhang, “Sentiment analysis of commodity reviews based on albert-lstm,” in Journal of Physics: Conference Series, vol. 1651, no. 1.   IOP Publishing, 2020, p. 012022.
  29. H. Zhu, “MetaAID: A flexible framework for developing metaverse applications via ai technology and human editing,” 2022.
  30. A. Qayyum, M. A. Butt, H. Ali, M. Usman, O. Halabi, A. Al-Fuqaha, Q. H. Abbasi, M. A. Imran, and J. Qadir, “Secure and trustworthy artificial intelligence-extended reality (ai-xr) for metaverses,” arXiv preprint arXiv:2210.13289, 2022.
  31. M. Seif, R. Tandon, and M. Li, “Wireless federated learning with local differential privacy,” in 2020 IEEE International Symposium on Information Theory (ISIT).   IEEE, 2020, pp. 2604–2609.
  32. A. Qayyum, J. Qadir, M. Bilal, and A. Al-Fuqaha, “Secure and robust machine learning for healthcare: A survey,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 156–180, 2020.
  33. A. Qayyum, M. Usama, J. Qadir, and A. Al-Fuqaha, “Securing connected & autonomous vehicles: Challenges posed by adversarial machine learning and the way forward,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 998–1026, 2020.
  34. A. Qayyum, A. Ijaz, M. Usama, W. Iqbal, J. Qadir, Y. Elkhatib, and A. Al-Fuqaha, “Securing machine learning in the cloud: A systematic review of cloud machine learning security,” Frontiers in big Data, vol. 3, p. 587139, 2020.
  35. J. Wei, J. Li, Y. Lin, and J. Zhang, “LDP-based social content protection for trending topic recommendation,” IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4353–4372, 2020.
  36. A. Qayyum, M. Bilal, M. Hadi, P. Capik, M. Caputo, H. Vohra, A. Al-Fuqaha, and J. Qadir, “Can we revitalize interventional healthcare with ai-xr surgical metaverses?” arXiv preprint arXiv:2304.00007, 2023.
  37. E. Bertino and N. Islam, “Botnets and internet of things security,” Computer, vol. 50, no. 2, pp. 76–79, 2017.
  38. Y. Wang, Z. Su, J. Ni, N. Zhang, and X. Shen, “Blockchain-empowered space-air-ground integrated networks: Opportunities, challenges, and solutions,” IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 160–209, 2021.
  39. R. U. Rasool, H. F. Ahmad, W. Rafique, A. Qayyum, and J. Qadir, “Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ml,” Journal of Network and Computer Applications, p. 103332, 2022.
  40. S. Liao, J. Wu, A. K. Bashir, W. Yang, J. Li, and U. Tariq, “Digital twin consensus for blockchain-enabled intelligent transportation systems in smart cities,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 22 619–22 629, 2021.
  41. Y. Zhou, F. R. Yu, J. Chen, and Y. Kuo, “Cyber-physical-social systems: A state-of-the-art survey, challenges and opportunities,” IEEE Communications Surveys & Tutorials, vol. 22, no. 1, pp. 389–425, 2019.
  42. P. Casey, I. Baggili, and A. Yarramreddy, “Immersive virtual reality attacks and the human joystick,” IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 2, pp. 550–562, 2019.
  43. X. Cheng, S. Zhang, W. Liu, and J. Mou, “Understanding visitors’ metaverse and in-person tour intentions during the COVID-19 pandemic: A coping perspective,” in Proceedings of the 2023 56th Hawaii international conference on system sciences (HICSS), 2023.
  44. C. Parker, S. Yoo, Y. Lee, J. Fredericks, A. Dey, Y. Cho, and M. Billinghurst, “Towards an inclusive and accessible metaverse,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023, pp. 1–5.
  45. I. A. Faisal, T. W. Purboyo, and A. S. R. Ansori, “A review of accelerometer sensor and gyroscope sensor in imu sensors on motion capture,” J. Eng. Appl. Sci, vol. 15, no. 3, pp. 826–829, 2019.
  46. S. A. Anand, C. Wang, J. Liu, N. Saxena, and Y. Chen, “Spearphone: A speech privacy exploit via accelerometer-sensed reverberations from smartphone loudspeakers,” arXiv preprint arXiv:1907.05972, 2019.
  47. S. Naval, A. Pandey, S. Gupta, G. Singal, V. Vinoba, and N. Kumar, “Pin inference attack: A threat to mobile security and smartphone-controlled robots,” IEEE Sensors Journal, vol. 22, no. 18, pp. 17 475–17 482, 2022.
  48. P. Tedeschi, S. Sciancalepore, and R. Di Pietro, “Ppca - privacy-preserving collision avoidance for autonomous unmanned aerial vehicles,” IEEE Transactions on Dependable and Secure Computing, pp. 1–1, 2022.
  49. A. K. Sikder, L. Babun, H. Aksu, and A. S. Uluagac, “Aegis: A context-aware security framework for smart home systems,” in Proceedings of the 35th Annual Computer Security Applications Conference, ser. ACSAC ’19.   New York, NY, USA: Association for Computing Machinery, 2019, p. 28–41. [Online]. Available: https://doi.org/10.1145/3359789.3359840
  50. A. K. Sikder, G. Petracca, H. Aksu, T. Jaeger, and A. S. Uluagac, “A survey on sensor-based threats and attacks to smart devices and applications,” IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 1125–1159, 2021.
  51. J. A. De Guzman, K. Thilakarathna, and A. Seneviratne, “Security and privacy approaches in mixed reality: A literature survey,” ACM Comput. Surv., vol. 52, no. 6, oct 2019. [Online]. Available: https://doi.org/10.1145/3359626
  52. J. Shang, S. Chen, J. Wu, and S. Yin, “ARSpy: Breaking location-based multi-player augmented reality application for user location tracking,” IEEE Transactions on Mobile Computing, vol. 21, no. 2, pp. 433–447, 2020.
  53. K. Rasheed, A. Qayyum, M. Ghaly, A. Al-Fuqaha, A. Razi, and J. Qadir, “Explainable, trustworthy, and ethical machine learning for healthcare: A survey,” Computers in Biology and Medicine, p. 106043, 2022.
  54. F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine learning in IoT security: Current solutions and future challenges,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686–1721, 2020.
  55. A. Azmoodeh, A. Dehghantanha, and K.-K. R. Choo, “Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning,” IEEE Transactions on Sustainable Computing, vol. 4, no. 1, pp. 88–95, 2019.
  56. E. K. Viegas, A. O. Santin, V. V. Cogo, and V. Abreu, “A reliable semi-supervised intrusion detection model: One year of network traffic anomalies,” in ICC 2020-2020 IEEE International Conference on Communications (ICC).   IEEE, 2020, pp. 1–6.
  57. M. S. Alam and S. T. Vuong, “Random forest classification for detecting android malware,” in 2013 IEEE international conference on green computing and communications and IEEE Internet of Things and IEEE cyber, physical and social computing.   IEEE, 2013, pp. 663–669.
  58. W. Zhou and B. Yu, “A cloud-assisted malware detection and suppression framework for wireless multimedia system in IoT based on dynamic differential game,” China Communications, vol. 15, no. 2, pp. 209–223, 2018.
  59. X. Liu, X. Du, X. Zhang, Q. Zhu, H. Wang, and M. Guizani, “Adversarial samples on android malware detection systems for IoT systems,” Sensors, vol. 19, no. 4, p. 974, Feb 2019. [Online]. Available: http://dx.doi.org/10.3390/s19040974
  60. J. Tian, B. Wang, J. Li, Z. Wang, B. Ma, and M. Ozay, “Exploring targeted and stealthy false data injection attacks via adversarial machine learning,” IEEE Internet of Things Journal, vol. 9, no. 15, pp. 14 116–14 125, 2022.
  61. X. Liu, R. Lu, J. Ma, L. Chen, and B. Qin, “Privacy-preserving patient-centric clinical decision support system on naïve bayesian classification,” IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 2, pp. 655–668, 2016.
  62. K. Malik, F. Rehman, T. Maqsood, S. Mustafa, O. Khalid, and A. Akhunzada, “Lightweight internet of things botnet detection using one-class classification,” Sensors, vol. 22, no. 10, p. 3646, May 2022. [Online]. Available: http://dx.doi.org/10.3390/s22103646
  63. H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “A deep recurrent neural network based approach for internet of things malware threat hunting,” Future Generation Computer Systems, vol. 85, pp. 88–96, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X1732486X
  64. E. B. Karbab, M. Debbabi, A. Derhab, and D. Mouheb, “Maldozer: Automatic framework for android malware detection using deep learning,” Digital Investigation, vol. 24, pp. S48–S59, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1742287618300392
  65. J. Su, D. V. Vasconcellos, S. Prasad, D. Sgandurra, Y. Feng, and K. Sakurai, “Lightweight classification of IoT malware based on image recognition,” in 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 02, 2018, pp. 664–669.
  66. P. Ghosh and R. Mitra, “Proposed ga-bfss and logistic regression based intrusion detection system,” in Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), 2015, pp. 1–6.
  67. R. Smethurst, “Digital identity wallets and their semantic contradictions,” 2023.
  68. M. Zichichi and G. Sorrentino, “Protecting digital identity in the metaverse: the case of access to a cinema in decentraland,” 2023.
  69. S. Wan, H. Lin, W. Gan, J. Chen, and P. S. Yu, “Web3: The next internet revolution,” arXiv preprint arXiv:2304.06111, 2023.
  70. Y. Xiao, L. Xu, C. Zhang, L. Zhu, and Y. Zhang, “Blockchain empowered privacy-preserving digital objects trading in metaverse,” IEEE MultiMedia, 2023.
  71. A. Amich and B. Eshete, “Explanation-guided diagnosis of machine learning evasion attacks,” in Security and Privacy in Communication Networks: 17th EAI International Conference, SecureComm 2021, Virtual Event, September 6–9, 2021, Proceedings, Part I 17.   Springer, 2021, pp. 207–228.
  72. J. Lin, L. Dang, M. Rahouti, and K. Xiong, “Ml attack models: Adversarial attacks and data poisoning attacks,” arXiv preprint arXiv:2112.02797, 2021.
  73. M. S. Elsayed, N.-A. Le-Khac, S. Dev, and A. D. Jurcut, “Ddosnet: A deep-learning model for detecting network attacks,” in 2020 IEEE 21st International Symposium on” A World of Wireless, Mobile and Multimedia Networks”(WoWMoM).   IEEE, 2020, pp. 391–396.
  74. W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Jiao, Y.-C. Liang, Q. Yang, D. Niyato, and C. Miao, “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031–2063, 2020.
  75. C. Ma, J. Li, M. Ding, H. H. Yang, F. Shu, T. Q. Quek, and H. V. Poor, “On safeguarding privacy and security in the framework of federated learning,” IEEE network, vol. 34, no. 4, pp. 242–248, 2020.
  76. K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning based on over-the-air computation,” in ICC 2019-2019 IEEE international conference on communications (ICC).   IEEE, 2019, pp. 1–6.
  77. X. Ying, “An overview of overfitting and its solutions,” Journal of Physics: Conference Series, vol. 1168, no. 2, p. 022022, Feb 2019. [Online]. Available: https://dx.doi.org/10.1088/1742-6596/1168/2/022022
  78. H. Li, J. Li, X. Guan, B. Liang, Y. Lai, and X. Luo, “Research on overfitting of deep learning,” in 2019 15th International Conference on Computational Intelligence and Security (CIS), 2019, pp. 78–81.
  79. N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A survey on bias and fairness in machine learning,” vol. 54, no. 6, 2021. [Online]. Available: https://doi.org/10.1145/3457607
  80. A. Amich and B. Eshete, “Explanation-guided diagnosis of machine learning evasion attacks,” in Security and Privacy in Communication Networks, J. Garcia-Alfaro, S. Li, R. Poovendran, H. Debar, and M. Yung, Eds.   Cham: Springer International Publishing, 2021, pp. 207–228.
  81. G. S. Collins, P. Dhiman, C. L. Andaur Navarro, J. Ma, L. Hooft, J. B. Reitsma, P. Logullo, A. L. Beam, L. Peng, B. Van Calster, M. van Smeden, R. D. Riley, and K. G. Moons, “Protocol for development of a reporting guideline (tripod-ai) and risk of bias tool (probast-ai) for diagnostic and prognostic prediction model studies based on artificial intelligence,” vol. 11, no. 7, 2021.
  82. M. Al-Rubaie and J. M. Chang, “Privacy-preserving machine learning: Threats and solutions,” IEEE Security & Privacy, vol. 17, no. 2, pp. 49–58, 2019.
  83. B. Liu, M. Ding, S. Shaham, W. Rahayu, F. Farokhi, and Z. Lin, “When machine learning meets privacy: A survey and outlook,” vol. 54, no. 2, 2021.
  84. P. W. Koh, S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. L. Phillips, I. Gao, T. Lee, E. David, I. Stavness, W. Guo, B. Earnshaw, I. Haque, S. M. Beery, J. Leskovec, A. Kundaje, E. Pierson, S. Levine, C. Finn, and P. Liang, “Wilds: A benchmark of in-the-wild distribution shifts,” in Proceedings of the 38th International Conference on Machine Learning, 2021, pp. 5637–5664.
  85. A. Jovanović and A. Milosavljević, “Vortex metaverse platform for gamified collaborative learning,” Electronics, vol. 11, no. 3, p. 317, 2022.
  86. H.-S. Cha and C.-H. Im, “Performance enhancement of facial electromyogram-based facial-expression recognition for social virtual reality applications using linear discriminant analysis adaptation,” Virtual Reality, vol. 26, no. 1, pp. 385–398, 2022.
  87. H. Hu, Z. Salcic, L. Sun, G. Dobbie, P. S. Yu, and X. Zhang, “Membership inference attacks on machine learning: A survey,” ACM Computing Surveys (CSUR), vol. 54, no. 11s, pp. 1–37, 2022.
  88. Y. Zhang, R. Jia, H. Pei, W. Wang, B. Li, and D. Song, “The secret revealer: Generative model-inversion attacks against deep neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 253–261.
  89. C. Yuan, X. Liu, and Z. Zhang, “The current status and progress of adversarial examples attacks,” in 2021 International Conference on Communications, Information System and Computer Engineering (CISCE).   IEEE, 2021, pp. 707–711.
  90. Y. Miao, C. Chen, L. Pan, Q.-L. Han, J. Zhang, and Y. Xiang, “Machine learning–based cyber attacks targeting on controlled information: A survey,” ACM Computing Surveys (CSUR), vol. 54, no. 7, pp. 1–36, 2021.
  91. K. G. Liakos, G. K. Georgakilas, S. Moustakidis, N. Sklavos, and F. C. Plessas, “Conventional and machine learning approaches as countermeasures against hardware trojan attacks,” Microprocessors and Microsystems, vol. 79, p. 103295, 2020.
  92. X. Zhang, C. Chen, Y. Xie, X. Chen, J. Zhang, and Y. Xiang, “A survey on privacy inference attacks and defenses in cloud-based deep neural network,” Computer Standards & Interfaces, p. 103672, 2022.
  93. Z. Lv, “Metaverse age: Scheduling strategies for digital resource management,” IEEE Consumer Electronics Magazine, 2022.
  94. T. Maksymyuk, J. Gazda, G. Bugár, V. Gazda, M. Liyanage, and M. Dohler, “Blockchain-empowered service management for the decentralized metaverse of things,” IEEE Access, vol. 10, pp. 99 025–99 037, 2022.
  95. S. Mishra, H. Arora, G. Parakh, and J. Khandelwal, “Contribution of blockchain in development of metaverse,” in 2022 7th International Conference on Communication and Electronics Systems (ICCES).   IEEE, 2022, pp. 845–850.
  96. Y. Luo, Z. Su, W. Zheng, Z. Chen, F. Wang, Z. Zhang, and J. Chen, “A novel memory-hard password hashing scheme for blockchain-based cyber-physical systems,” ACM Transactions on Internet Technology (TOIT), vol. 21, no. 2, pp. 1–21, 2021.
  97. R. Di Pietro and S. Cresci, “Metaverse: security and privacy issues,” in 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA).   IEEE, 2021, pp. 281–288.
  98. S. Hilt, F. Maggi, C. Perine, L. Remorin, M. Rösler, and R. Vosseler, “Caught in the act: Running a realistic factory honeypot to capture real threats,” Trend Micro, Shibuya City, Japan, White Paper, 2020.
  99. S. Mackenzie, “Criminology towards the metaverse: Cryptocurrency scams, grey economy and the technosocial,” The British Journal of Criminology, vol. 62, no. 6, pp. 1537–1552, 2022.
  100. N. Carlini and A. Terzis, “Poisoning and backdooring contrastive learning,” arXiv preprint arXiv:2106.09667, 2021.
  101. R. L. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosystems,” Communications of the ACM, vol. 21, no. 2, pp. 120–126, 1978.
  102. F. Armknecht, C. Boyd, C. Carr, K. Gjøsteen, A. Jäschke, C. A. Reuter, and M. Strand, “A guide to fully homomorphic encryption,” Cryptology ePrint Archive, 2015.
  103. D. Tourky, M. ElKawkagy, and A. Keshk, “Homomorphic encryption the “holy grail” of cryptography,” in 2016 2nd IEEE International Conference on Computer and Communications (ICCC).   IEEE, 2016, pp. 196–201.
  104. A. M. Al-Ghaili, H. Kasim, N. M. Al-Hada, Z. Hassan, M. Othman, T. J. Hussain, R. M. Kasmani, and I. Shayea, “A review of metaverse’s definitions, architecture, applications, challenges, issues, solutions, and future trends,” IEEE Access, 2022.
  105. E. Sarkar, E. Chielle, G. Gursoy, L. Chen, M. Gerstein, and M. Maniatakos, “Privacy-preserving cancer type prediction with homomorphic encryption,” Scientific Reports, vol. 13, no. 1, p. 1661, 2023.
  106. P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in Advances in Cryptology—EUROCRYPT’99: International Conference on the Theory and Application of Cryptographic Techniques Prague, Czech Republic, May 2–6, 1999 Proceedings 18.   Springer, 1999, pp. 223–238.
  107. Z. Brakerski and V. Vaikuntanathan, “Fully homomorphic encryption from ring-LWE and security for key dependent messages,” in Advances in Cryptology–CRYPTO 2011: 31st Annual Cryptology Conference, Santa Barbara, CA, USA, August 14-18, 2011. Proceedings 31.   Springer, 2011, pp. 505–524.
  108. R. Podschwadt, D. Takabi, P. Hu, M. H. Rafiei, and Z. Cai, “A survey of deep learning architectures for privacy-preserving machine learning with fully homomorphic encryption,” IEEE Access, vol. 10, pp. 117 477–117 500, 2022.
  109. K. Han and D. Ki, “Better bootstrapping for approximate homomorphic encryption,” in Topics in Cryptology–CT-RSA 2020: The Cryptographers’ Track at the RSA Conference 2020, San Francisco, CA, USA, February 24–28, 2020, Proceedings.   Springer, 2020, pp. 364–390.
  110. A. Blanco-Justicia, J. Domingo-Ferrer, S. Martínez, D. Sánchez, A. Flanagan, and K. E. Tan, “Achieving security and privacy in federated learning systems: Survey, research challenges and future directions,” Engineering Applications of Artificial Intelligence, vol. 106, p. 104468, 2021.
  111. T. Ha, T. K. Dang, T. T. Dang, T. A. Truong, and M. T. Nguyen, “Differential privacy in deep learning: An overview,” in 2019 International Conference on Advanced Computing and Applications (ACOMP), 2019, pp. 97–102.
  112. M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016, pp. 308–318.
  113. H. B. Braiek and F. Khomh, “On testing machine learning programs,” Journal of Systems and Software, vol. 164, p. 110542, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0164121220300248
  114. S. Ho, A. Reddy, S. Venkatesan, R. Izmailov, R. Chadha, and A. Oprea, “Data sanitization approach to mitigate clean-label attacks against malware detection systems,” in MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM), 2022, pp. 993–998.
  115. X. Zhou, Z. Xu, C. Wang, and M. Gao, “Ppmlac: High performance chipset architecture for secure multi-party computation.”   Association for Computing Machinery, 2022.
  116. “Safeguarding cross-silo federated learning with local differential privacy,” Digital Communications and Networks, vol. 8, no. 4, pp. 446–454, 2022.
  117. W. Jin, Y. Yao, S. Han, C. Joe-Wong, S. Ravi, S. Avestimehr, and C. He, “Fedml-he: An efficient homomorphic-encryption-based privacy-preserving federated learning system,” arXiv preprint arXiv:2303.10837, 2023.
  118. A.-H. Rasha, T. Li, W. Huang, J. Gu, and C. Li, “Federated learning in smart cities: Privacy and security survey,” Information Sciences, 2023.
  119. J.-W. Lee, H. Kang, Y. Lee, W. Choi, J. Eom, M. Deryabin, E. Lee, J. Lee, D. Yoo, Y.-S. Kim et al., “Privacy-preserving machine learning with fully homomorphic encryption for deep neural network,” IEEE Access, vol. 10, pp. 30 039–30 054, 2022.
  120. J. Zhao, Y. Chen, and W. Zhang, “Differential privacy preservation in deep learning: Challenges, opportunities and solutions,” IEEE Access, vol. 7, pp. 48 901–48 911, 2019.
  121. B. Hitaj, G. Ateniese, and F. Perez-Cruz, “Deep models under the GAN: information leakage from collaborative deep learning,” in Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, 2017, pp. 603–618.
  122. H. Fereidooni, S. Marchal, M. Miettinen, A. Mirhoseini, H. Möllering, T. D. Nguyen, P. Rieger, A.-R. Sadeghi, T. Schneider, H. Yalame et al., “Safelearn: secure aggregation for private federated learning,” in 2021 IEEE Security and Privacy Workshops (SPW).   IEEE, 2021, pp. 56–62.
  123. C. Zhao, S. Zhao, M. Zhao, Z. Chen, C.-Z. Gao, H. Li, and Y.-a. Tan, “Secure multi-party computation: theory, practice and applications,” Information Sciences, vol. 476, pp. 357–372, 2019.
  124. P. P. Liang, T. Liu, L. Ziyin, N. B. Allen, R. P. Auerbach, D. Brent, R. Salakhutdinov, and L.-P. Morency, “Think locally, act globally: Federated learning with local and global representations,” arXiv preprint arXiv:2001.01523, 2020.
  125. A. Qayyum, K. Ahmad, M. A. Ahsan, A. Al-Fuqaha, and J. Qadir, “Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge,” IEEE Open Journal of the Computer Society, vol. 3, pp. 172–184, 2022.
  126. T. Li, M. Sanjabi, A. Beirami, and V. Smith, “Fair resource allocation in federated learning,” arXiv preprint arXiv:1905.10497, 2019.
  127. W. Du, X. Zeng, M. Yan, and M. Zhang, “Efficient federated learning via variational dropout,” 2018.
  128. Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, jan 2019. [Online]. Available: https://doi.org/10.1145/3298981
  129. M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed, “Federated learning: A survey on enabling technologies, protocols, and applications,” IEEE Access, vol. 8, pp. 140 699–140 725, 2020.
  130. Z. Li, V. Sharma, and S. P. Mohanty, “Preserving data privacy via federated learning: Challenges and solutions,” IEEE Consumer Electronics Magazine, vol. 9, no. 3, pp. 8–16, 2020.
  131. Y. Liu, Z. Ma, X. Liu, S. Ma, S. Nepal, and R. Deng, “Boosting privately: Privacy-preserving federated extreme boosting for mobile crowdsensing,” arXiv preprint arXiv:1907.10218, 2019.
  132. X. Yao, T. Huang, C. Wu, R. Zhang, and L. Sun, “Towards faster and better federated learning: A feature fusion approach,” in 2019 IEEE International Conference on Image Processing (ICIP).   IEEE, 2019, pp. 175–179.
  133. T. Sun, D. Li, and B. Wang, “Decentralized federated averaging,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
  134. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics.   PMLR, 2017, pp. 1273–1282.
  135. Z. Zhang, C. Guan, H. Chen, X. Yang, W. Gong, and A. Yang, “Adaptive privacy-preserving federated learning for fault diagnosis in internet of ships,” IEEE Internet of Things Journal, vol. 9, no. 9, pp. 6844–6854, 2021.
  136. E. U. Soykan, L. Karaçay, F. Karakoç, and E. Tomur, “A survey and guideline on privacy enhancing technologies for collaborative machine learning,” IEEE Access, vol. 10, pp. 97 495–97 519, 2022.
  137. X. Wang, H. Xue, X. Liu, and Q. Pei, “A privacy-preserving edge computation-based face verification system for user authentication,” IEEE Access, vol. 7, pp. 14 186–14 197, 2019.
  138. J. Liang, Z. Qin, J. Ni, X. Lin, and X. Shen, “Efficient and privacy-preserving outsourced SVM classification in public cloud,” in ICC 2019-2019 IEEE International Conference on Communications (ICC).   IEEE, 2019, pp. 1–6.
  139. M. Hashemian, F. Razzazi, H. Zarrabi, and M. S. Moin, “A privacy-preserving distributed transfer learning in activity recognition,” Telecommunication Systems, vol. 72, no. 1, pp. 69–79, 2019.
  140. L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. D. Tygar, “Adversarial machine learning,” in Proceedings of the 4th ACM workshop on Security and artificial intelligence, 2011, pp. 43–58.
  141. G. Sun, Y. Cong, J. Dong, Q. Wang, L. Lyu, and J. Liu, “Data poisoning attacks on federated machine learning,” IEEE Internet of Things Journal, vol. 9, no. 13, pp. 11 365–11 375, 2021.
  142. X. Guo, “Federated learning for data security and privacy protection,” in 2021 12th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP).   IEEE, 2021, pp. 194–197.
  143. M. Alazab, S. P. RM, M. Parimala, P. K. R. Maddikunta, T. R. Gadekallu, and Q.-V. Pham, “Federated learning for cybersecurity: concepts, challenges, and future directions,” IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3501–3509, 2021.
  144. S. Alfeld, X. Zhu, and P. Barford, “Data poisoning attacks against autoregressive models,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1, 2016.
  145. G. Xu and Z. Zhou, “Assessing the efficiency of financial supply chain for Chinese commercial banks: a two-stage ar-dea model,” Industrial Management & Data Systems, vol. 121, no. 4, pp. 894–920, 2021.
  146. J. M. D. Delgado, L. Oyedele, P. Demian, and T. Beach, “A research agenda for augmented and virtual reality in architecture, engineering and construction,” Advanced Engineering Informatics, vol. 45, p. 101122, 2020.
  147. I. Ouali, M. B. Halima, and W. Ali, “Augmented reality for scene text recognition, visualization and reading to assist visually impaired people,” Procedia Computer Science, vol. 207, pp. 158–167, 2022.
  148. A. Qayyum, M. U. Janjua, and J. Qadir, “Making federated learning robust to adversarial attacks by learning data and model association,” Computers & Security, vol. 121, p. 102827, 2022.
  149. H. Ali, R. T. Javed, A. Qayyum, A. AlGhadhban, M. Alazmi, A. Alzamil, K. Al-utaibi, and J. Qadir, “Spam-das: Secure and privacy-aware misinformation detection as a service,” 2022.
  150. V. Nair, G. M. Garrido, and D. Song, “Exploring the unprecedented privacy risks of the metaverse,” arXiv preprint arXiv:2207.13176, 2022.
  151. A. Winkler, J. Won, and Y. Ye, “QuestSim: Human motion tracking from sparse sensors with simulated avatars,” in SIGGRAPH Asia 2022 Conference Papers, 2022, pp. 1–8.
  152. M. Keshk, B. Turnbull, N. Moustafa, D. Vatsalan, and K.-K. R. Choo, “A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks,” IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 5110–5118, 2019.
  153. Z. Yi, Y. Jiao, W. Dai, G. Li, H. Wang, and Y. Xu, “A Stackelberg incentive mechanism for wireless federated learning with differential privacy,” IEEE Wireless Communications Letters, vol. 11, no. 9, pp. 1805–1809, 2022.
  154. M. Yang, H. Cheng, F. Chen, X. Liu, M. Wang, and X. Li, “Model poisoning attack in differential privacy-based federated learning,” Information Sciences, 2023.
  155. G. Munilla Garrido, V. Nair, and D. Song, “SoK: Data privacy in virtual reality,” arXiv e-prints, pp. arXiv–2301, 2023.
  156. X. Zhou, Z. Xu, C. Wang, and M. Gao, “PPMLAC: high performance chipset architecture for secure multi-party computation,” in Proceedings of the 49th Annual International Symposium on Computer Architecture, 2022, pp. 87–101.
  157. I. Mavridou, E. Seiss, T. Kostoulas, C. Nduka, and E. Balaguer-Ballester, “Towards an effective arousal detection system for virtual reality,” in Proceedings of the Workshop on Human-Habitat for Health (H3): Human-Habitat Multimodal Interaction for Promoting Health and Well-Being in the Internet of Things Era, 2018, pp. 1–6.
  158. A. Al Arafat, Z. Guo, and A. Awad, “VR-spy: A side-channel attack on virtual key-logging in VR headsets,” in 2021 IEEE Virtual Reality and 3D User Interfaces (VR).   IEEE, 2021, pp. 564–572.
  159. B. David-John, D. Hosfelt, K. Butler, and E. Jain, “A privacy-preserving approach to streaming eye-tracking data,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 5, pp. 2555–2565, 2021.
  160. A. K. Chaudhary and J. B. Pelz, “Privacy-preserving eye videos using rubber sheet model,” in ACM Symposium on Eye Tracking Research and Applications, 2020, pp. 1–5.
  161. P. P. Tricomi, F. Nenna, L. Pajola, M. Conti, and L. Gamberini, “You can’t hide behind your headset: User profiling in augmented and virtual reality,” IEEE Access, 2023.
  162. J. Liebers, M. Abdelaziz, L. Mecke, A. Saad, J. Auda, U. Gruenefeld, F. Alt, and S. Schneegass, “Understanding user identification in virtual reality through behavioral biometrics and the effect of body normalization,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1–11.
  163. Z. Ling, Z. Li, C. Chen, J. Luo, W. Yu, and X. Fu, “I know what you enter on gear VR,” in 2019 IEEE Conference on Communications and Network Security (CNS).   IEEE, 2019, pp. 241–249.
  164. D. Maloney, S. Zamanifard, and G. Freeman, “Anonymity vs. familiarity: Self-disclosure and privacy in social virtual reality,” in Proceedings of the 26th ACM Symposium on Virtual Reality Software and Technology, 2020, pp. 1–9.
  165. A. Cheng, P. Wang, X. S. Zhang, and J. Cheng, “Differentially private federated learning with local regularization and sparsification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10 122–10 131.
  166. C. Fung, C. J. Yoon, and I. Beschastnikh, “Mitigating Sybils in federated learning poisoning,” arXiv preprint arXiv:1808.04866, 2018.
  167. B. Haber, “The digital ephemeral turn: Queer theory, privacy, and the temporality of risk,” Media, Culture & Society, vol. 41, no. 8, pp. 1069–1087, 2019.
  168. Y. K. Dwivedi, L. Hughes, A. M. Baabdullah, S. Ribeiro-Navarrete, M. Giannakis, M. M. Al-Debei, D. Dennehy, B. Metri, D. Buhalis, C. M. Cheung et al., “Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” International Journal of Information Management, vol. 66, p. 102542, 2022.
  169. K. Alspach, “Why the fate of the metaverse could hang on its security,” 2022. [Online]. Available: https://venturebeat.com/uncategorized/why-the-fate-of-the-metaverse-could-hang-on-its-security/
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