相关实验视频
Updated: Jun 20, 2025

13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
9.0K
自学激活功能,以提高保护隐私的准确性 卷积神经网络与同型加密
Bernardo Pulido-Gaytan1, Andrei Tchernykh1,2
1Computer Science Department, CICESE Research Center, Ensenada, BC, Mexico.
PloS one
|July 22, 2024
概括
本研究介绍了自学激活函数 (SLAF),以增强隐私保护的同型加密 (CNN-HE) 卷积神经网络. SLAF 提高了安全云数据处理的准确性和性能.
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:
- 云计算的采用需要强大的隐私保护技术来处理数据.
- 同型加密的卷积神经网络 (CNN-HE) 现有的隐私保护方法在准确性和性能方面面临挑战.
- 激活功能是CNN中的关键组件,影响其学习能力.
研究的目的:
- 提出和评估自学激活函数 (SLAFs),以提高保护隐私的CNN-HE模型的准确性和性能.
- 从理论上证明SLAFs在接近连续激活功能的可行性.
- 引入两种新型的CNN-HE模型,其中包括SLAF:CNN-HE-SLAF和CNN-HE-SLAF-R.
主要方法:
- 开发了SLAFs作为可训练的多项式,在训练期间更新系数,独立于突触权重.
- 提出了两种CNN-HE架构:CNN-HE-SLAF (所有激活被SLAF取代) 和CNN-HE-SLAF-R (SLAF在初始培训后进行调整).
- 从理论上证明了SLAFs对基于多项式度的任何连续激活函数的近似能力.
主要成果:
- 在MNIST数据集上实现了99.38%的准确性,与使用ReLU.LU的非同型CNN相比.
- 与最先进的CNN-HE CryptoNets相比,CNN-HE-SLAF-R模型显示了更好的准确性 (99.21%) 和显著的性能增长 (6.26倍更快).
- 在同态加密框架内,SLAFs可以实现任务特定和CNN特定的特征学习.
结论:
- 自学激活功能提供了一种可行的方法,可以提高保护隐私CNN-HE的准确性和性能.
- 在同型加密设置中,SLAF为传统激活函数提供了灵活和高效的替代方案.
- 拟议的CNN-HE-SLAF模型代表了对加密数据的安全和高效深度学习的重大进展.
相关概念视频
Convolution Properties I
145
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
145
Convolution Properties II
179
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
179
Convolution: Math, Graphics, and Discrete Signals
240
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...
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...
240
Associative Learning
329
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Classical conditioning, also known...
329
Improving Translational Accuracy
9.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.8K
Deconvolution
147
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
147

