Kerem Arikan (Binghamton University), Abraham Farrell (Binghamton University), Williams Zhang Cen (Binghamton University), Jack McMahon (Binghamton University), Barry Williams (Binghamton University), Yu David Liu (Binghamton University), Nael Abu-Ghazaleh (University of California, Riverside), Dmitry Ponomarev (Binghamton University)

Protection of cache hierarchies from side-channel attacks is critical for building secure systems, particularly the ones using Trusted Execution Environments (TEEs). In this paper, we consider the problem of efficient and secure fine-grained partitioning of cache hierarchies and propose a framework, called Secure Hierarchies for TEEs (TEE-SHirT). In the context of a three-level cache system, TEE-SHirT consists of partitioned shared last-level cache, partitioned private L2 caches, and non-partitioned L1 caches that are flushed on context switches and system calls. Efficient and correct partitioning requires careful design. Towards this goal, TEE-SHirT makes three contributions: 1) we demonstrate how the hardware structures used for holding cache partitioning metadata can be effectively virtualized to avoid flushing of cache partition content on context switches and system calls; 2) we show how to support multi-threaded enclaves in TEE-SHirT, addressing the issues of coherency and consistency that arise with both intra-core and inter-core data sharing; 3) we develop a formal security model for TEE-SHirT to rigorously reason about the security of our design.

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Jiafan Wang (Data61, CSIRO), Sherman S. M. Chow (The Chinese University of Hong Kong)

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Yang Yang (School of Computing and Information Systems, Singapore Management University, Singapore), Robert H. Deng (School of Computing and Information Systems, Singapore Management University, Singapore), Guomin Yang (School of Computing and Information Systems, Singapore Management University, Singapore), Yingjiu Li (Department of Computer Science, University of Oregon, USA), HweeHwa Pang (School of Computing and Information Systems,…

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Differentially Private Dataset Condensation

Tianhang Zheng (University of Missouri-Kansas City), Baochun Li (University of Toronto)

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