Ganxiang Yang (Shanghai Jiao Tong University), Chenyang Liu (Shanghai Jiao Tong University), Zhen Huang (Shanghai Jiao Tong University), Guoxing Chen (Shanghai Jiao Tong University), Hongfei Fu (Shanghai Jiao Tong University), Yuanyuan Zhang (Shanghai Jiao Tong University), Haojin Zhu (Shanghai Jiao Tong University)

Trusted Execution Environments (TEE) have been widely adopted as a protection approach for security-critical applications. Although feature extensions have been previously proposed to improve the usability of enclaves, their provision patterns are still confronted with security challenges. This paper presents Palantir, a verifiable multi-layered inter-enclave privilege model for secure feature extensions to enclaves. Specifically, a parent-children inter-enclave relationship, with which a parent enclave is granted two privileged permissions, the Execution Control and Spatial Control, over its children enclaves to facilitate secure feature extensions, is introduced. Moreover, by enabling nesting parent-children relationships, Palantir achieves multi-layered privileges (MLP) that allow feature extensions to be placed in various privilege layers following the Principle of Least Privilege. To prove the security of Palantir, we verified that our privilege model does not break or weaken the security guarantees of enclaves by building and verifying a formal model named $text{TAP}^{infty}$. Furthermore, We implemented a prototype of Palantir on Penglai, an open-sourced RISC-V TEE platform. The evaluation demonstrates the promising performance of Palantir in runtime overhead $(<5%)$ and startup latencies.

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Decoupling Permission Management from Cryptography for Privacy-Preserving Systems

Ruben De Smet (Department of Engineering Technology (INDI), Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel), Tom Godden (Department of Engineering Technology (INDI), Vrije Universiteit Brussel), Kris Steenhaut (Department of Engineering Technology (INDI), Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel), An Braeken (Department of Engineering Technology (INDI), Vrije Universiteit Brussel)

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LLM-xApp: A Large Language Model Empowered Radio Resource Management...

Xingqi Wu (University of Michigan-Dearborn), Junaid Farooq (University of Michigan-Dearborn), Yuhui Wang (University of Michigan-Dearborn), Juntao Chen (Fordham University)

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Abdullah AlHamdan (CISPA Helmholtz Center for Information Security), Cristian-Alexandru Staicu (CISPA Helmholtz Center for Information Security)

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ASGARD: Protecting On-Device Deep Neural Networks with Virtualization-Based Trusted...

Myungsuk Moon (Yonsei University), Minhee Kim (Yonsei University), Joonkyo Jung (Yonsei University), Dokyung Song (Yonsei University)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)