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相关概念视频

Understanding Memory01:19

Understanding Memory

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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
302
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

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In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

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Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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High-resolution Episcopic Microscopy HREM - Simple and Robust Protocols for Processing and Visualizing Organic Materials
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低内存足迹的加密图像分类使用完全同态加密.

Lorenzo Rovida1, Alberto Leporati1

  • 1Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca, 336, Milan, 20126, Italy.

International journal of neural systems
|March 22, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种保护隐私的图像分类方法,使用具有残余网络的完全同型加密 (FHE). 它可以在标准硬件上进行准确的,加密的图像分析,保护敏感数据.

关键词:
加密的神经网络.同型型的加密方式.安全机器学习安全机器学习

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科学领域:

  • 计算机科学 计算机科学
  • 密码学 密码学 密码学 密码学
  • 机器学习 机器学习

背景情况:

  • 深度神经网络 (DNN) 擅长图像分类,但对敏感数据的隐私问题提出了担忧.
  • 当前的方法往往忽视了培训和部署模型对个人或机密图像的隐私影响.
  • 需要安全的计算技术来保护机器学习过程中的数据.

研究的目的:

  • 使用机器学习和密码学开发一个保护隐私的图像分类系统.
  • 实现一个能够处理加密图像的剩余网络 (ResNet) 模型.
  • 确保只有预期的用户才能解密和查看分类结果.

主要方法:

  • 探索机器学习和密码学之间的交叉点,特别是完全同型加密 (FHE).
  • 为FHE计算优化剩余网络架构的开发.
  • 使用Cheon-Kim-Kim-Song (CKKS) 方案进行近似加密计算的实施.
  • 电路设计以尽量减少内存需求和计算开销.

主要成果:

  • 提出并实施了一种基于FHE的新型剩余网络,用于加密图像分类.
  • 与之前的工作相比,拟议的电路大大降低了内存使用量.
  • 一个加密的ResNet20模型在笔记本电脑上在不到五分钟的时间内在CIFAR-10数据集上实现了91.67%的准确性.
  • 获得的准确性与普通模型的准确性 (92.60%) 相当.

结论:

  • 完全同型加密 (FHE) 可以有效地与深度神经网络相结合,用于保护隐私的图像分类.
  • 开发的系统为分析敏感图像数据提供了切实可行的解决方案,而不会损害隐私.
  • 该方法证明了计算效率,内存使用量和分类准确性之间的可行的权衡.