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1. Confidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey Study

   https://doi.org/10.1002/spe.3398  

   Zobaed, Sm; Amini_Salehi, Mohsen (January 2025, Software: Practice and Experience)

   ABSTRACT BackgroundConfidential computing has gained prominence due to the escalating volume of data‐driven applications (e.g., machine learning and big data) and the acute desire for secure processing of sensitive data, particularly across distributed environments, such as the edge‐to‐cloud continuum. ObjectiveProvided that the works accomplished in this emerging area are scattered across various research fields, this paper aims at surveying the fundamental concepts and cutting‐edge software and hardware solutions developed for confidential computing using trusted execution environments, homomorphic encryption, and secure enclaves. MethodsWe underscore the significance of building trust at both the hardware and software levels and delve into their applications, particularly for regular and advanced machine learning (ML) (e.g., large language models (LLMs), computer vision) applications. ResultsWhile substantial progress has been made, there are some barely‐explored areas that need extra attention from the researchers and practitioners in the community to improve confidentiality aspects, develop more robust attestation mechanisms, and address vulnerabilities of the existing trusted execution environments. ConclusionProviding a comprehensive taxonomy of the confidential computing landscape, this survey enables researchers to advance this field to ultimately ensure the secure processing of users' sensitive data across a multitude of applications and computing tiers. 

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2. ENCIDER: Detecting Timing and Cache Side Channels in SGX Enclaves and Cryptographic APIs

   https://doi.org/10.1109/TDSC.2022.3160346  

   Yavuz, Tuba; Fowze, Farhaan; Hernandez, Grant; Bai, Ken Yihang; Butler, Kevin; Tian, Dave Jing (March 2022, IEEE Transactions on Dependable and Secure Computing)

   Confidential computing aims to secure the code and data in use by providing a Trusted Execution Environment (TEE) for applications using hardware features such as Intel SGX.Timing and cache side-channel attacks, however, are often outside the scope of the threat model, although once exploited they are able to break all the default security guarantees enforced by hardware. Unfortunately, tools detecting potential side-channel vulnerabilities within applications are limited and usually ignore the strong attack model and the unique programming model imposed by Intel SGX. This paper proposes a precise side-channel analysis tool, ENCIDER, detecting both timing and cache side-channel vulnerabilities within SGX applications via inferring potential timing observation points and incorporating the SGX programming model into analysis. ENCIDER uses dynamic symbolic execution to decompose the side-channel requirement based on the bounded non-interference property and implements byte-level information flow tracking via API modeling. We have applied ENCIDER to 4 real-world SGX applications, 2 SGX crypto libraries, and 3 widely-used crypto libraries, and found 29 timing side channels and 73 code and data cache side channels. We have reported our findings to the corresponding parties, e.g., Intel and ARM, who have confirmed most of the vulnerabilities detected. 

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3. GraFBoost: Using Accelerated Flash Storage for External Graph Analytics

   https://doi.org/https://doi.org/10.1109/isca.2018.00042  

   Jun, Sang-Woo; Wright, Andy; Zhang, Sizhuo; Xu, Shuotao; Arvind, Arvind (June 2018, Proceedings)

   We describe GraFBoost, a flash-based architecture with hardware acceleration for external analytics of multi-terabyte graphs. We compare the performance of GraFBoost with 1 GB of DRAM against various state-of-the-art graph analytics software including FlashGraph, running on a 32-thread Xeon server with 128 GB of DRAM. We demonstrate that despite the relatively small amount of DRAM, GraFBoost achieves high performance with very large graphs no other system can handle, and rivals the performance of the fastest software platforms on sizes of graphs that existing platforms can handle. Unlike in-memory and semi-external systems, GraFBoost uses a constant amount of memory for all problems, and its performance decreases very slowly as graph sizes increase, allowing GraFBoost to scale to much larger problems than possible with existing systems while using much less resources on a single-node system. The key component of GraFBoost is the sort-reduce accelerator, which implements a novel method to sequentialize fine-grained random accesses to flash storage. The sort-reduce accelerator logs random update requests and then uses hardware-accelerated external sorting with interleaved reduction functions. GraFBoost also stores newly updated vertex values generated in each superstep of the algorithm lazily with the old vertex values to further reduce I/O traffic. We evaluate the performance of GraFBoost for PageRank, breadth-first search and betweenness centrality on our FPGA-based prototype (Xilinx VC707 with 1 GB DRAM and 1 TB flash) and compare it to other graph processing systems including a pure software implementation of GrapFBoost. 

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4. A Verified Confidential Computing as a Service Framework for Privacy Preservation

   Chen, Hongbo; Chen, Haobin; Sun, Mingshen; Li, Kang; Chen, Zhaofeng; Wang, XiaoFeng (August 2023, the Proceedings of the 32nd USENIX Security Symposium)

   Joe Calandrino and Carmela Troncoso (Ed.)

   As service providers are moving to the cloud, users are forced to provision sensitive data to the cloud. Confidential computing leverages hardware Trusted Execution Environment (TEE) to protect data in use, no longer requiring users’ trust to the cloud. The emerging service model, Confidential Computing as a Service (CCaaS), is adopted by service providers to offer service similar to the Function-as-a-Serivce manner. However, privacy concerns are raised in CCaaS, especially in multi-user scenarios. CCaaS need to assure the data providers that the service does not leak their privacy to any unauthorized parties and clear their data after the service. To address such privacy concerns with security guarantees, we first formally define the security objective, Proof of Being Forgotten (PoBF), and prove under which security constraints PoBF can be satisfied. Then, these constraints serve as guidelines in the implementation of the PoBF-compliant Framework (PoCF). PoCF consists of a generic library for different hardware TEEs, CCaaS prototype enclaves, and a verifier to prove PoBF-compliance. PoCF leverages Rust’s robust type system and security features, to construct a verified state machine with privacy-preserving contracts. Last, the experiment results show that the protections introduced by PoCF incur minor runtime performance overhead. 

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5. A Data Prefetcher-Based 1000-Core RISC-V Processor for Efficient Processing of Graph Neural Networks

   https://doi.org/10.1109/LCA.2025.3545799  

   Khan, Omer (February 2025, IEEE Computer Architecture Letters)

   Graphs-based neural networks have seen tremendous adoption to perform complex predictive analytics on massive real-world graphs. The trend in hardware acceleration has identified significant challenges with harnessing graph locality and workload imbalance due to ultrasparse and irregular matrix computations at a massively parallel scale. State-of-the-art hardware accelerators utilize massive multithreading and asynchronous execution in GPUs to achieve parallel performance at high power consumption. This paper aims to bridge the power-performance gap using the energy efficiency-centric RISC-V ecosystem. A 1000-core RISC-V processor is proposed to unlock massive parallelism in the graphs-based matrix operators to achieve a low-latency data access paradigm in hardware to achieve robust power-performance scaling. Each core implements a single-threaded pipeline with a novel graph-aware data prefetcher at the 1000 cores scale to deliver an average 20× performance per watt advantage over state-of-the-art NVIDIA GPU. 

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