Leia: A Lightweight Cryptographic Neural Network Inference System at the Edge

Liu, Xiaoning; Wu, Bang Ye; Yuan, Xingliang; Yi, Xun

doi:10.1109/tifs.2021.3138611

Cited by 25 publications

(7 citation statements)

References 25 publications

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“…4) Security Computation: Security computation has also been widely adopted to protect data privacy in general data analytics systems and services. There are three main types of techniques for security computation, including Trusted Executive Environment (TEE) [213], [214], Homomorphic Encryption (HE) [215], [216], and Multi-party Secure Computation (MPC) [217], [218], [219]. Note that, since the underlying operations (e.g., matrix multiplication) in GNNs are similar to those in ordinary DNNs, most of the existing security computation methods in DNNs can be directly extended to GNNs.…”

Section: Methods Category Taskmentioning

confidence: 99%

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Zhang¹,

Wu²,

Yuan³

et al. 2022

Preprint

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Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics. However, task performance is not the only requirement for GNNs. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks, unexplainable discrimination against disadvantaged groups, or excessive resource consumption in edge computing environments. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness. To this end, we propose a comprehensive roadmap to build trustworthy GNNs from the view of the various computing technologies involved. In this survey, we introduce basic concepts and comprehensively summarise existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability, and environmental well-being. Additionally, we highlight the intricate cross-aspect relations between the above six aspects of trustworthy GNNs. Finally, we present a thorough overview of trending directions for facilitating the research and industrialisation of trustworthy GNNs.

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Section: Methods Category Taskmentioning

confidence: 99%

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Zhang¹,

Wu²,

Yuan³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Different from the above works, we foucs on reducing the SLO violation rate caused by the interference of multimodel. In addition, some edge inference frameworks involve privacy protection [21], [22] and edge-cloud collaborative [23], [24], respectively. These works are orthogonal to BCEdge that can alleviate privacy and resource constraints.…”

Section: A Model-level Dnn Inference Servicementioning

confidence: 99%

BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms

Zhang¹,

Li²,

Zhao³

et al. 2023

Preprint

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<p>As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time. Such edge platforms with multiple DNN models pose new challenges for scheduler designs. First, each request may have different service level objectives (SLOs) to improve quality of service (QoS). Second, the edge platforms should be able to efficiently schedule multiple heterogeneous DNN models so that system utilization can be improved. To meet these two goals, this paper proposes BCEdge, a novel learning-based scheduling framework that takes adaptive batching and concurrent execution of DNN inference services on edge platforms. We define a utility function to evaluate the trade-off between throughput and latency. The scheduler in BCEdge leverages maximum entropy-based deep reinforcement learning (DRL) to maximize utility by 1) co-optimizing batch size and 2) the number of concurrent models automatically. Our prototype implemented on different edge platforms shows that the proposed BCEdge enhances utility by up to 37.6% on average, compared to state-of-the-art solutions, while satisfying SLOs.</p>

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“…Well known semiconductor companies like Xilinx, Intel, and Apple are showing interest in BNNs due to these advantages [43,84,91]. Owing to their low memory footprint and lightweight nature of operations, BNNs are considered attractive for edge applications such as FPGA-accelerators [25], cryptographic neural network inference systems [50], and for designing low-bitwidth ConvNets [102], among many other applications. Despite their low-bitwidth operations, the accuracy obtained by BNNs is comparable to that obtained by full-precision neural networks.…”

Section: Binarized Neural Networkmentioning

confidence: 99%

Guarding Machine Learning Hardware Against Physical Side-channel Attacks

Dubey

Cammarota

Suresh

et al. 2022

J. Emerg. Technol. Comput. Syst.

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Machine learning (ML) models can be trade secrets due to their development cost. Hence, they need protection against malicious forms of reverse engineering (e.g., in IP piracy). With a growing shift of ML to the edge devices, in part for performance and in part for privacy benefits, the models have become susceptible to the so-called physical side-channel attacks. ML being a relatively new target compared to cryptography poses the problem of side-channel analysis in a context that lacks published literature. The gap between the burgeoning edge-based ML devices and the research on adequate defenses to provide side-channel security for them thus motivates our study. Our work develops and combines different flavors of side-channel defenses for ML models in the hardware blocks. We propose and optimize the first defense based on Boolean masking . We first implement all the masked hardware blocks. We then present an adder optimization to reduce the area and latency overheads. Finally, we couple it with a shuffle-based defense. We quantify that the area-delay overhead of masking ranges from 5.4× to 4.7× depending on the adder topology used and demonstrate a first-order side-channel security of millions of power traces. Additionally, the shuffle countermeasure impedes a straightforward second-order attack on our first-order masked implementation.

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Leia: A Lightweight Cryptographic Neural Network Inference System at the Edge

Cited by 25 publications

References 25 publications

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms

Guarding Machine Learning Hardware Against Physical Side-channel Attacks

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