Metaverse: Security and Privacy Concerns

Abstract

The term “metaverse”, a three-dimensional virtual universe similar to the real realm, has always been full of imagination since it was put forward in the 1990s. Recently, it is possible to realize the metaverse with the continuous emergence and progress of various technologies, and thus it has attracted extensive attention again. It may bring a lot of benefits to human society such as reducing discrimination, eliminating individual differences, and socializing. However, everything has security and privacy concerns, which is no exception for the metaverse. In this article, we firstly analyze the concept of the metaverse and propose that it is a super virtual-reality (VR) ecosystem compared with other VR technologies. Then, we carefully analyze and elaborate on possible security and privacy concerns from four perspectives: user information, communication, scenario, and goods, and immediately, the potential solutions are correspondingly put forward. Meanwhile, we propose the need to take advantage of the new buckets effect to comprehensively address security and privacy concerns from a philosophical perspective, which hopefully will bring some progress to the metaverse community.

Keywords

• Metaverse
• privacy
• security
• virtual universe
• buckets effect

References

1. N. Stephenson, Snow crash: A novel. Spectra, 2003.
2. L.-H. Lee, T. Braud, P. Zhou, L. Wang, D. Xu, Z. Lin, A. Kumar, C.Bermejo, and P. Hui, “All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda,” arXiv preprint arXiv:2110.05352, 2021.
3. B. Falchuk, S. Loeb, and R. Neff, “The social metaverse: Battle for privacy,” IEEE Technol. Soc. Mag., vol. 37, no. 2, pp. 52–61, 2018.
4. Z. Chen, H. Cao, Y. Deng, X. Gao, J. Piao, F. Xu, Y. Zhang, and Y. Li, “Learning from home: A mixed-methods analysis of live streaming based remote education experience in chinese colleges during the covid-19 pandemic,” in Conf. Hum. Fact. Comput. Syst. Proc., ser. CHI ’21, 2021.
5. H. Liu, X. Yao, T. Yang, and H. Ning, “Cooperative privacy preservation for wearable devices in hybrid computing-based smart health,” IEEE Internet Things J., vol. 6, no. 2, pp. 1352–1362, 2019.
6. J. Fox, M. Gilbert, and W. Y. Tang, “Player experiences in a massively multiplayer online game: A diary study of performance, motivation, and social interaction,” New Media Soc., vol. 20, no. 11, pp. 4056–4073, 2018.
7. Q. Zhu, M. Chen, C.-W. Wong, and M. Wu, “Adaptive multi-trace carving for robust frequency tracking in forensic applications,” IEEE Trans. Inf. Forensic Secur., vol. 16, pp. 1174–1189, 2021.
8. H. Wu, X. Tian, Y. Gong, X. Su, M. Li, and F. Xu, “DAPter: Preventing user data abuse in deep learning inference services,” in Proc. World Wide Web Conf., 2021, pp. 1017–1028.

9. Y. Zhang, R. Zhao, X. Xiao, R. Lan, Z. Liu, and X. Zhang, “Hf-tpe: High-fidelity thumbnail- preserving encryption,” IEEE Trans. Circuits Syst. Video Technol., vol. 32, no. 3, pp. 947–961, 2022.
10. N. Vishwamitra, H. Hu, F. Luo, and L. Cheng, “Towards understanding and detecting cyberbullying in real-world images,” in IEEE Int. Conf. Mach. Learn. Appl., 2021.
11. M. Begum and M. S. Uddin, “Digital image watermarking techniques: A review,” Information, vol. 11, no. 2, p. 110, 2020.
12. J. Li, J. Wu, J. Li, A. K. Bashir, M. J. Piran, and A. Anjum, “Blockchain-based trust edge knowledge inference of multi-robot systems for collab-orative tasks,” IEEE Commun. Mag., vol. 59, no. 7, pp. 94–100, 2021.
13. S. Lee, M. Kim, J. Lee, R.-H. Hsu, and T. Q. S. Quek, “Is blockchain suitable for data freshness? an age-of-information perspective,” IEEE Netw., vol. 35, no. 2, pp. 96–103, 2021.