Collaborative Research: SaTC: CORE: Small: Securing Brain-inspired Hyperdimensional Computing against Design-time and Run-time Attacks for Edge Devices

协作研究：SaTC：核心：小型：保护类脑超维计算免受边缘设备的设计时和运行时攻击

• 批准号: 2326597
• 负责人: Xiaolin Xu
• 金额: $ 30万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326597&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

Many computing applications depend on machine learning (ML) algorithms that analyze patterns in data and make predictions about new data they encounter. Many recent advances in these machine learning classifiers use approaches based on neural networks; however, neural networks often require large amounts of data, memory, and processing power. Brain-inspired hyperdimensional computing (HDC) has emerged in recent years as a less resource-heavy approach to building classifiers that are well-suited for smaller computing devices that have less computing power. However, just like other ML classifier architectures, HDC models may be threatened by attackers who want to degrade the models' performance, insert backdoor "triggers" that let attackers take control of devices by presenting secret inputs, or steal the models themselves. However, these security risks in HDC models are not as well-studied as HDC performance. This project's goal is to close that gap through a better understanding of HDC security vulnerabilities and defenses. This includes analyzing the space of possible attacks on HDC models, drawing parallels between attacks and defenses in neural networks and those in HDC models, and developing defenses that are as effective, efficient, and lightweight as the HDC models themselves so they can too be deployed in devices with limited computing power.This project paves the way for HDC-based inference on edge devices by systematically investigating the attack surface for HDC computing, from design time to run time and from algorithm to hardware. First, it explores the vulnerabilities associated with HDC and systematically defines its unique attack surface. Accordingly, it investigates critical threats against HDC model performance and privacy from adversarial input, model perturbation, and reverse engineering. Second, it explores effective and efficient defense strategies by incorporating algorithmic-, hardware-, and system-level methods. A key insight and tool in the proposed work are methods for relating neural network-based models and HDC models; this will allow for comparative studies as well as open possibilities for adapting existing attacks and defenses on neural network-based architectures to HDC contexts. The scientific outcomes will help reshape HDC-enabled computing systems toward greater security and robustness. The project also contains a significant educational component and provides abundant opportunities to nurture and attract students from under-represented groups to engage in computer science and computer science research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

许多计算应用程序依赖于机器学习（ML）算法，这些算法分析数据中的模式并对它们遇到的新数据进行预测。这些机器学习分类器的许多最新进展使用基于神经网络的方法;然而，神经网络通常需要大量的数据，内存和处理能力。近年来，大脑启发的超维计算（HDC）作为一种资源较少的方法出现，用于构建非常适合计算能力较低的小型计算设备的分类器。然而，就像其他ML分类器架构一样，HDC模型可能会受到攻击者的威胁，他们希望降低模型的性能，插入后门“触发器”，让攻击者通过提供秘密输入来控制设备，或者窃取模型本身。然而，HDC模型中的这些安全风险并没有像HDC性能那样得到充分研究。该项目的目标是通过更好地了解HDC安全漏洞和防御来缩小这一差距。这包括分析HDC模型上可能的攻击空间，绘制神经网络和HDC模型中的攻击和防御之间的相似之处，并开发有效，高效，和轻量级的HDC模型本身，所以他们也可以部署在设备与有限的计算能力。这个项目铺平了道路，HDC-通过系统地研究HDC计算的攻击面，从设计时到运行时，从算法到硬件，基于边缘设备的推理。首先，它探讨了与HDC相关的漏洞，并系统地定义了其独特的攻击面。因此，它调查了对抗性输入，模型扰动和逆向工程对HDC模型性能和隐私的关键威胁。其次，它通过结合算法，硬件和系统级方法来探索有效和高效的防御策略。拟议工作中的一个关键见解和工具是将基于神经网络的模型和HDC模型相关联的方法;这将允许进行比较研究，并为基于神经网络的架构上的现有攻击和防御适应HDC上下文提供可能性。科学成果将有助于重塑支持HDC的计算系统，使其具有更高的安全性和鲁棒性。该项目还包含一个重要的教育组成部分，并提供了大量的机会，培养和吸引学生从代表性不足的群体从事计算机科学和计算机科学研究。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

MirrorNet: A TEE-Friendly Framework for Secure On-Device DNN Inference

• DOI: 10.1109/iccad57390.2023.10323746
• 发表时间: 2023-10
• 期刊: 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)
• 作者: Ziyu Liu;Yukui Luo;Shijin Duan;Tong Zhou;Xiaolin Xu

Achieving Certified Robustness for Brain-Inspired Low-Dimensional Computing Classifiers

• DOI: 10.1109/infocomwkshps57453.2023.10225774
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
• 作者: Fangfang Yang;Shijin Duan;Xiaolin Xu;Shaolei Ren

AQ2PNN: Enabling Two-party Privacy-Preserving Deep Neural Network Inference with Adaptive Quantization

• DOI: 10.1145/3613424.3614297
• 发表时间: 2023-10
• 期刊: 2023 56th IEEE/ACM International Symposium on Microarchitecture (MICRO)
• 作者: Yukui Luo;Nuo Xu;Hongwu Peng;Chenghong Wang;Shijin Duan;Kaleel Mahmood;Wujie Wen;Caiwen Ding;Xiaolin Xu