Abstract
The advent of 6G networks is anticipated to introduce a myriad of new technology enablers, including heterogeneous radio, RAN softwarization, multi-vendor deployments, and AI-driven network management, which is expected to broaden the existing threat landscape, demanding for more sophisticated security controls. At the same time, privacy forms a fundamental pillar in the EU development activities for 6G. This decentralized and globally connected environment necessitates robust privacy provisions that encompass all layers of the network stack.
In this paper, we present PRIVATEER’s approach for enabling “privacy-first” security enablers for 6G networks. PRIVATEER aims to tackle four major privacy challenges associated with 6G security enablers, i.e., i) processing of infrastructure and network usage data, ii) security-aware orchestration, iii) infrastructure and service attestation and iv) cyber threat intelligence sharing. PRIVATEER addresses the above by introducing several innovations, including decentralised robust security analytics, privacy-aware techniques for network slicing and service orchestration and distributed infrastructure and service attestation mechanisms.
This work has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the EU Horizon Europe programme PRIVATEER under Grant Agreement No. 101096110. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the EU or SNS JU.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
AMD-Xilinx, “Using Encryption and Authentication to Secure an UltraScale/UltraScale+ FPGA Bitstream”. https://docs.xilinx.com/r/en-US/xapp1267-encryp-efuse-program/Using-Encryption-and-Authentication-to-Secure-an-UltraScale/UltraScale-FPGA-Bitstream-Application-Note. Accessed 15 May 2023
Abraham, S.S.: Fairlof: fairness in outlier detection. Data Sci. Eng. 6, 485–499 (2021)
Araújo, R., Pinto, A.: Secure remote storage of logs with search capabilities. J. Cybersecur. Privacy 1(2), 340–364 (2021). https://doi.org/10.3390/jcp1020019. https://www.mdpi.com/2624-800X/1/2/19
Benčić, F.M., Skočir, P., Žarko, I.P.: DL-tags: DLT and smart tags for decentralized, privacy-preserving, and verifiable supply chain management. IEEE Access 7, 46198–46209 (2019)
Bernardos, C.J., Uusitalo, M.A.: European vision for the 6G network ecosystem (2021). https://doi.org/10.5281/zenodo.5007671
Brockners, F., Bhandari, S., Mizrahi, T., Dara, S., Youell, S.: Proof of transit. Internet Engineering Task Force, Internet-Draft draft-ietf-sfcproof-of-transit-06 (2020)
Das, A., Rad, P.: Opportunities and challenges in explainable artificial intelligence (XAI): a survey. arXiv preprint arXiv:2006.11371 (2020)
Fernandes, R., Bugla, S., Pinto, P., Pinto, A.: On the performance of secure sharing of classified threat intelligence between multiple entities. Sensors 23(2), 914 (2023). https://doi.org/10.3390/s23020914. www.mdpi.com/1424-8220/23/2/914
Hu, H., Liu, Y., Wang, Z., Lan, C.: A distributed fair machine learning framework with private demographic data protection. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 1102–1107. IEEE (2019)
Iwahana, K., Yanai, N., Cruz, J.P., Fujiwara, T.: SPGC: integration of secure multiparty computation and differential privacy for gradient computation on collaborative learning. J. Inf. Process. 30, 209–225 (2022)
Jagielski, M., et al.: Differentially private fair learning. In: International Conference on Machine Learning, pp. 3000–3008. PMLR (2019)
Jiang, W., Han, B., Habibi, M.A., Schotten, H.D.: The road towards 6G: a comprehensive survey. IEEE Open J. Commun. Soci. 2, 334–366 (2021)
Katz, M., Pirinen, P., Posti, H.: Towards 6G: getting ready for the next decade. In: 2019 16th International Symposium on Wireless Communication Systems (ISWCS), pp. 714–718. IEEE (2019)
Kourtis, M.A., et al.: Conceptual evaluation of a 5G network slicing technique for emergency communications and preliminary estimate of energy trade-off. Energies 14(21), 6876 (2021)
Lee, Y.L., Loo, J., Chuah, T.C., Wang, L.C.: Dynamic network slicing for multitenant heterogeneous cloud radio access networks. IEEE Trans. Wireless Commun. 17(4), 2146–2161 (2018)
Li, R., et al.: Deep reinforcement learning for resource management in network slicing. IEEE Access 6, 74429–74441 (2018)
Lux, Z.A., Thatmann, D., Zickau, S., Beierle, F.: Distributed-ledger-based authentication with decentralized identifiers and verifiable credentials. In: 2020 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), pp. 71–78. IEEE (2020)
Mothukuri, V., Parizi, R.M., Pouriyeh, S., Huang, Y., Dehghantanha, A., Srivastava, G.: A survey on security and privacy of federated learning. Futur. Gener. Comput. Syst. 115, 619–640 (2021)
Nguyen, V.L., Lin, P.C., Cheng, B.C., Hwang, R.H., Lin, Y.D.: Security and privacy for 6G: a survey on prospective technologies and challenges. IEEE Commun. Surv. Tutorials 23(4), 2384–2428 (2021)
Pentyala, S., et al.: Training differentially private models with secure multiparty computation. arXiv preprint arXiv:2202.02625 (2022)
Shekhar, S., Shah, N., Akoglu, L.: Fairod: fairness-aware outlier detection. In: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (2021)
Steiner, R.V., Lupu, E.: Attestation in wireless sensor networks: a survey. ACM Comput. Surv. (CSUR) 49(3), 1–31 (2016)
Wagner, C., Dulaunoy, A., Wagener, G., Iklody, A.: Misp: The design and implementation of a collaborative threat intelligence sharing platform. In: Proceedings of the 2016 ACM on Workshop on Information Sharing and Collaborative Security, pp. 49–56 (2016)
You, X., et al.: Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. Sci. China Inf. Sci. 64, 1–74 (2021)
Zhang, H., Davidson, I.: Towards fair deep anomaly detection. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (2021)
Zhang, J., Chen, J., Wu, D., Chen, B., Yu, S.: Poisoning attack in federated learning using generative adversarial nets. In: 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 374–380. IEEE (2019)
Zhang, J., Qu, G.: Recent attacks and defenses on FPGA-based systems. ACM Trans. Reconfigurable Technol. Syst. (TRETS) 12(3), 1–24 (2019)
Zhang, S.: An overview of network slicing for 5G. IEEE Wirel. Commun. 26(3), 111–117 (2019)
Zikria, Y.B., Ali, R., Afzal, M.K., Kim, S.W.: Next-generation internet of things (IoT): opportunities, challenges, and solutions. Sensors 21(4), 1174 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Masouros, D. et al. (2023). Towards Privacy-First Security Enablers for 6G Networks: The PRIVATEER Approach. In: Silvano, C., Pilato, C., Reichenbach, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2023. Lecture Notes in Computer Science, vol 14385. Springer, Cham. https://doi.org/10.1007/978-3-031-46077-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-46077-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46076-0
Online ISBN: 978-3-031-46077-7
eBook Packages: Computer ScienceComputer Science (R0)