# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 3 Proposed Methods
### Experiment Settings
#### 3.4.2 Models and Algorithms for Experiment  
As mentioned above, we use VGG, ResNet, and MobileNet as the test models. Linear regression or other prediction models are not used because they contain very few operators and therefore cannot simulate the large number of computational requests in real scenarios. Here, we briefly introduce these three models:  
1. **VGG**. VGG was proposed by the group of Visual Geometry Group at Oxford. The network is a related work at ILSVRC 2014, and the main work is to demonstrate that increasing the depth of the network can affect the final performance of the network to some extent.There are several structures of VGG, namely, VGG13, VGG16, andVGG19, which are not fundamentally different from each other, but the network depth is not the same. One improvement of VGG16 over AlexNet [47] is the use of several consecutive \(3\times 3\) convolutional kernels instead of the larger convolutional kernels in AlexNet (\(11\times 11\), \(7\times 7\), \(5\times 5\)). For a given perceptual field (the local size of the input picture with respect to the output), the use of stacked small convolutional kernels is preferable to the use of large convolutional kernels, because the multilayer nonlinear layers can increase the network depth to ensure learning more complex patterns, and at a smaller cost (fewer parameters). In brief, in VGG, three \(3\times 3\) convolutional kernels are used instead of \(7\times 7\) convolutional kernels, and two \(3\times 3\) convolutional kernels are used instead of \(5\times 5\) convolutional kernels. The main purpose of this is to improve the depth of the network while guaranteeing to have the same perceptual field, which improves the neural network to some extent. For example, the superposition of one layer of three \(3\times 3\) convolutional kernels with step size 1 can be viewed as a perceptual field of size 7 (in fact, it means that three \(3\times 3\) successive convolutions are equivalent to one \(7\times 7\) convolution), whose total parameters are \(3\times(9\times 5^{2})\), and if the \(7\times 7\) convolutional kernels are used directly, the total parameters are \(49\times 5^{2}\), where C refers to the number of channels of input and output. Obviously, \(27\times 5^{2}\) is less than \(49\times C^{2}\), i.e., fewer parameters; and the \(3\times 3\) convolution kernel facilitates better preservation of image properties. 2. **ResNet**. The ResNet network is a reference to the VGG19 network with modifications based on it and the inclusion of residual units through a short-circuiting mechanism. The changes are mainly reflected in that ResNet directly uses the convolution with \(stride=2\) for downsampling and replaces the fully connected layer with the global average pool layer. An important design principle of ResNet is that the number of feature maps is doubled when the feature map size is reduced by half, which maintains the complexity of the network layers. ResNet adds a short-circuiting mechanism between every two layers compared to the normal network, which results in residual learning. For the 18-layer and 34-layer ResNet, it performs residual learning between two layers, and when the network is deeper, it performs residual learning between three layers, and the three convolutional kernels are \(1\times 1,3\times 3\), and \(1\times 1\), respectively, and a noteworthy point is that the number of feature maps in the hidden layer is relatively small and is \(\frac{1}{4}\) of the output.The number of feature maps is \(\frac{1}{4}\) of the number of output maps. 3. **MobileNet**. The basic unit of MobileNet is the depthwise separable convolution, a structure that has been used in the Inception model before. Depthwise convolution is actually a factorised convolution that can be decomposed into two smaller operations: depthwise convolution and pointwise convolution.