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相关概念视频

Neural Circuits01:25

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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Norton's Theorem01:14

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Norton's theorem is a fundamental principle stating that a linear two-terminal circuit can be substituted with an equivalent circuit, which comprises a current source (ⅠN) in parallel with a resistor (RN). Here, ⅠN represents the short-circuit current flowing through the terminals, and RN stands for the input or equivalent resistance at the terminals when all independent sources are deactivated. This implies that the circuit illustrated in Figure (a) can be exchanged with the...
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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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深度神经网络量化框架,有效防御会员推理攻击.

Azadeh Famili1, Yingjie Lao1

  • 1The Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括

这项研究引入了一种新的量子化方法,以增强神经网络的隐私,防止会员推断攻击 (MIA). 新方法显著提高了对MIA的抵抗力,保护了敏感的培训数据.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 网络安全 网络安全

背景情况:

  • 边缘设备上的机器学习面临着计算和隐私方面的挑战.
  • 成员推断攻击 (MIA) 通过推断培训组成员身份来威胁用户数据隐私.
  • 保护培训数据对于医疗保健等对隐私敏感的应用程序至关重要.

研究的目的:

  • 调查量子化对隐私泄露的影响.
  • 提出一种新的量子化方法,专门设计用于增强对MIA的抵抗力.
  • 为神经网络开发针对MIA的防御机制.

主要方法:

  • 利用量子化对隐私泄露的影响.
  • 开发和提出一个新的量子化框架,优先考虑MIA阻力.
  • 在基准数据集和各种模型架构上评估拟议的方法.

主要成果:

  • 拟议的量子化方法增强了神经网络对MIA的抵抗力.
  • 实验结果显示,与全位宽模型相比,精度,回忆和F1分数有所改善.
  • 对于Cifar10上的ResNet,MIA攻击精度降低了14%,真实阳性率降低了37%,成员的F1得分降低了39%.
关键词:
深度神经网络是一个神经网络.成员关系推断攻击模型量化定量化的模型.隐私 隐私 隐私 隐私 隐私 隐私安全的安全的安全的安全的安全.

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结论:

  • 量化可以被利用来加强对MIA的隐私.
  • 拟议的量子化框架有效地防御MIA,而不会影响性能.
  • 这种方法为增强机器学习部署中的数据隐私提供了可行的解决方案.