Tolga O. Atalay (A2 Labs LLC), Tianyuan Yu (UCLA), Lixia Zhang (UCLA), Angelos Stavrou (A2 Labs LLC)

Cellular core networks are deployed as a set of Virtual Network Functions (VNFs) to dynamically provide customized connectivity for specific use cases. These VNFs are software-based applications whose trust management and security rely on well-established network domain solutions and certificate-based trust mechanisms. As VNFs are frequently redeployed, migrated, and scaled across a diverse ecosystem, the reliance on static trust solutions introduces bottlenecks and operational complexities. This approach to trust undermines the ability to ensure seamless, secure, and efficient interactions in a rapidly evolving cellular ecosystem. Addressing these challenges necessitates a fundamental shift toward an architectural foundation that inherently embeds security and trust into the communication fabric. Named Data Networking (NDN) offers such a foundation by focusing on data-centric security, where trust is embedded within the data itself rather than being dependent on external entities or channels. Leveraging named entities, NDN makes it possible to construct fine-grained trust relationships across cellular domains, tenants, and network slices. This paradigm shift enables the cellular core to move beyond static security solutions, providing a cohesive and scalable framework for managing trust in next-generation cellular networks. In this paper, we propose the adoption of the NDN network model to address the limitations of traditional approaches and achieve seamless security that evolves with the dynamic demands of 5G and beyond networks.

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Towards Better CFG Layouts

Jack Royer (CentraleSupélec), Frédéric TRONEL (CentraleSupélec, Inria, CNRS, University of Rennes), Yaëlle Vinçont (Univ Rennes, Inria, CNRS, IRISA)

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Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

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Retrofitting XoM for Stripped Binaries without Embedded Data Relocation

Chenke Luo (Wuhan University), Jiang Ming (Tulane University), Mengfei Xie (Wuhan University), Guojun Peng (Wuhan University), Jianming Fu (Wuhan University)

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