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Introduction to Keras Regularization

Keras regularization allows us to apply the penalties in the parameters of layer activities at the optimization time. Those penalties were summed into the function of loss, and it will optimize the network. It applies on a per-layer basis. The exact API depends on the layer, but multiple layers contain a unified API. The layer will expose arguments of 3 keywords.

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Key Takeaways

Suppose we need to configure the regularization using multiple arguments, then implement the subclass into the keras regularization.

We can also implement the class method and get the config to support the serialization. We can also use regularization parameters.

What is Keras Regularization?

The keras regularization prevents the over-fitting penalizing model from containing large weights. There are two popular parameters available, i.e., L1 and L2. L1 is nothing but the Lasso, and L2 is called Ridge. Both of these parameters are defined at the time of learning the linear regression. When working with tensorflow, we can implement the regularization using an optimizer. We are adding regularization to our code by adding a parameter name as kernel_regularizer. While adding L2 regularization, we need to pass the keras regularizers.l2 () function.

This function takes one parameter, which contains the strength of regularization. We pass L1 regularizers by replacing the l2 function with the l1 function. Suppose we need to use L2 and l1 regularization this is called the elastic net. The weight regularization provides an approach to reducing the overfitting of neural network models for deep learning. Activity regularization encourages the neural network to learn the sparse features of internal representations for the raw observations. It is common to seek the representation of spark known for autoencoders called sparse encoders.

How to Add Keras Regularization?

It will generally reduce the model overfitting and help the model generalize. The regularization is a penalized model for overfitting, as we know it has two parameters. Below we are using the l1 parameter for adding keras regularization.

Below steps shows how we can add keras regularization as follows:

Code:

python -m pip install tensorflow python –m pip install keras

Output:

2. After installing the module of keras and tensorflow now we are checking the installation by importing both modules as follows.

Code:

import tensorflow as tf from keras.layers import Dense

Output:

3. After checking the installation now in this step we are importing the required model which was used in it. Basically, we are importing the dense, sequential, l1, and activation modules. We are importing the dense module from the layers library, a sequential module from the library, an l1 module from the regularizers library, and an activation module from the layers library.

Code:

from sklearn.datasets import make_circles ….. from keras.layers import Activation

Output:

4. After importing the dataset now in this step we are preparing the dataset for it. We are preparing the dataset by using x and y values. Also, we are defining the value of X_train, y_train, X_test, and y_test as follows.

Code:

X, y = make_circles() train = 25 X_train, X_test = X[] y_train, y_test = y[]

Output:

5. After creating the dataset in this step we are creating the neural network model and adding the regularizer into the input layer as follows. We are adding a sequential model and defining the dense layer as follows.

Code:

mod = Sequential() mod.add() mod.add(Activation('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()

Output:

Keras Regularization Layer

The weight regularization layer of keras is applying penalties to the parameters of layers. The weight regularization layer will expose three keyword arguments as follows:

Kernel Regularizer

Bias Regularizer

Activity Regularizer

The below example shows keras weight regularization layer as follows. This layer is dividing the input batch size.

Code:

from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2 (1e-5) ) ten = chúng tôi (shape = (7, 7)) * 3.0 out = we_lay(ten) print(tf.math.reduce_sum (we_lay.losses))

Output:

The L1 and L2 regularizers are available as part of a module of regularizers. The below example shows the L1 class regularizers module.

Code:

from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense ( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2(1e-5) ) ten = tf.keras.regularizers.L1(l1=0.01 * 3.0) print (tf.math.reduce_sum (we_lay.losses))

Output:

The below example shows the L1 class regularizers module as follows. We are importing the layers and regularizers model.

Code:

from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2 (1e-5) ) ten = tf.keras.regularizers.L2 (l2 = 0.01 * 3.0) print(tf.math.reduce_sum(we_lay.losses))

Output:

Examples of Keras Regularization Example #1

In the below example we are using L2 arguments.

Code:

from sklearn.datasets import make_circles ….. from keras.layers import Activation X, y = make_circles() train = 25 X_train, X_test = X [] y_train, y_test = y [] mod = Sequential() mod.add() mod.add(Activation ('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()

Output:

Example #2

In the below example, we are using L1 arguments.

Code:

from sklearn.datasets import make_circles ….. from keras.layers import Activation X, y = make_circles() train = 35 X_train, X_test = X[] y_train, y_test = y[] mod = Sequential() mod.add() mod.add(Activation('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()

Output:

FAQ

Given below are the FAQs mentioned:

Q1. What is the use of keras regularization?

Answer: It is the technique for preventing the model from large weights. The regularization category is applied to the per-layer basis.

Q2. How many types of weight regularization are in keras?

Answer: Basically there are multiple types of weight regularization like vector norms, L1 and L2. It will require the hyper parameter which is configured.

Q3. Which modules do we need to import at the time of using keras regularization?

Answer: We need to import the keras and tensorflow module at the time of using it. Also, we need to import is a dense layer.

Conclusion

There are two popular keras regularization parameters available i.e. L1 and L2. In that L1 is nothing but the Lasso and L2 is called Ridge. It allows us to apply the penalties to the parameters of layer activities at the time of optimization.

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Techniques And Types Of Storage Virtualization

Introduction to Storage Virtualization

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Types of Storage Virtualization

Widely these storage systems provide two kinds of access to our system either block or file-based access.

So we can broadly classify our storage virtualization as:

1. Block Virtualization

In block virtualization, we basically separate our logical storage from that of the physical so that the user/administrator can access without having to access the physical storage, basically doing this way helps the administrator in giving a lot of flexibility in managing different storage.

2. File Virtualization

In File virtualization, it basically removes the dependencies caused in accessing the data at file level to that of the location where they are actually present. This basically helps in overcoming the challenges faced with network-attached storage and they also help in optimizing the storage usage and also help us to do some file migrations in a non-disruptive way.

Methods of Virtualization

Virtualization typically refers to the pooling of different available storage and maintaining them in single storage in virtual environment, recent technologies such as hyper-converged infrastructure makes use of not only virtual storage but also power and network as well.

1. Host-Based Virtualization Approach 2. Array-Based Virtualization Approach 3. Network-Based Virtualization Approach Configurations of Storage Virtualization

Ideally, we have two different ways in configuring our storage virtualization they are:

1. In-Band Approach (Symmetric)

In this method, we store the virtual environment configuration in the data path itself as in the data as well as the control flow. This kind of solution is considered easy/simple to implement as we do not use any kind of software. We do different levels of abstraction inside the data path. These kinds of solutions help us to improve our device’s performance majorly and also prolong the useful life of the devices. One of the examples of an in-band based solution is that IBM’s total storage area network volume controller.

2. Out-Band Approach (Asymmetric)

In this approach, the implementation of the virtual environment is done outside of the data path as in the data flow and the control flow are separated which can be achieved by separating our Metadata from data and putting them in different places. This kind of virtualization involves in transferring all the tables to a Metadata controller which has all the Metadata files. By separating both the flows we achieve the usage of complete bandwidth that is offered by the storage area network.

Benefits of Storage Virtualization

Now that we have seen what is storage virtualization and its types and also how do we implement them, now let us see some of the benefits of going to storage virtualization:

Our data does not get compromised easily even if the host fails as we store our data in a different and convenient place.

It is easy for us to protect, provide and use our data as we implement some level of abstraction in our storage.

Additional functions such as recovery, duplication, replication, etc. can be done with ease.

Conclusion

By now from the article you would have understood quite a lot about storage virtualization and its techniques, pros, and cons. It is necessary for us to go towards these kinds of virtualization approaches as it helps in reducing the complexity of the way data is stored and also helps the storage administrator in performing tasks such as disaster recovery, backup or archival of data easily in less amount of time.

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Guide To Caffe Tensorflow Framework In Detail

Introduction to Caffe TensorFlow

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How does Caffe TensorFlow work?

It is an open-source GitHub repository which consumes prototxt file as an input parameter and converts it to a python file. Thus, with this, the Caffe model can be easily deployed in the TensorFlow environment. The pre-trained baseline models can be easily validated by using a validator file written in Python. For the older Caffe Models, upgrade_net_proto_text and upgrade_net_proto_binary files have to be used for first upgrading them to the latest version supported by Caffe and then following the subsequent steps mentioned inline to deploy it to the TensorFlow environment. It has one constraint that is the user needs to have a Python 2.7 environment to access it. Also, Caffe and TensorFlow models cannot be invoked concurrently. So, a two-stage process is followed. First, the parameters are extracted and converted using the converter file, which is then fed into the TensorFlow in the last stage. Also, the user’s border values and padding have to be taken care of as it is handled differently in both Caffe and TensorFlow.

The below steps describe how the user can use the above repository on his/her local machine.

To install Caffe-TensorFlow, use git clone command with the repository path to map it to your local folder.

It uses TensorFlow GPU environment by default which consumes more memory. To avoid getting into this, uninstall the default environment and install TensorFlow CPU.

Convert the Caffe model into TensorFlow by using python executable command with the chúng tôi file. It will take verbose parameters like Caffe model path, the prototxt file path, the output path where weights and other parameters related to the model are stored, the converted code path and a standalone output path which has a pb file generated if the executed command is successful. This file stores the model weights and the corresponding architecture.

Following steps can be followed by the user:

The model weights can be combined into a single file using a combine python file available as a gist on GitHub. The associated weights in it can be loaded into the user’s TensorFlow computational graph.

The ordering of complex layers used in TensorFlow and Caffe models are different. E.g. the concatenation of the LSTM gates is ordered differently for both TensorFlow and Caffe. Thus, the user needs to have a deeper look at the source code for both the frameworks, which is open-source.

A potential rudimentary first up approach which can be used easily by the user is as follows:

The Caffe Model weights can be exported into a NumPy n-dimensional matrix.

A simple model example can be run for the preliminary N layers of the Caffe Model. The corresponding output can be stored in a flat-file.

The user can load the above weights into his/her TensorFlow computational graph.

Step 2 can be repeated for the TensorFlow computational graph.

The corresponding output can be compared with the output stored in the flat file.

If the output does not match, then the user can check whether the above steps were executed correctly or not.

N’s value can be incremented after every iteration, and the above steps are repeated for its updated value.

Benefits of Caffe TensorFlow

The Caffe Models are stored into a repository called Caffe Model Zoo. This is accessed by the researchers, academicians, scientists, students etc. all over the world. The corresponding models associated with it can be easily converted into TensorFlow. This makes it computationally faster, cheaper, less memory-intensive etc. Also, it increases the user’s flexibility and usage as the user does not have to implement the same Caffe Model into TensorFlow from scratch. It has also been used to train ImageNet models with a fairly good amount of accuracy. It can be in image classification, speech processing, Natural Language Processing, detecting facial landmarks etc. where Convolutional Networks, LSTM, Bi-LSTM models etc. are used.

Conclusion

The Caffe-TensorFlow Model finds its usage across all industry domains as model deployment is required for both popular deep learning frameworks. However, the user needs to be wary of its limitations and overcome the same while developing the model in Caffe and deploying it in TensorFlow.

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Power Bi Compression Techniques In Dax Studio

In this tutorial, you’ll learn about the different Power BI compression techniques in DAX Studio that help optimize your report.

After data is loaded segment by segment by the Analysis Services in Power BI Power Pivot and SSAS, two events occur. The first one is that they try to use different encoding methods to compress columns to reduce the overall RAM size. The second one is that they try to fund out the best sort order that places repeating values together. This method also increases compression and in turn, reduces the pressure on the memory.

There are different compression techniques used by Analysis Services. This tutorial covers three methods, in particular, namely, Value Encoding, Run Length Encoding, and Dictionary Encoding. In the last section of this tutorial, it’ll cover how to sort order works in Analysis Services.

The first one is called Value Encoding.

Value Encoding seeks out a mathematical relationship between each value in a column to reduce memory. Here’s an example in Microsoft Excel:

This column requires 16,384 bits in order to store the values.

To compute the bits required, first use the MAX() function in Excel to get the highest value in the columns. In this case, it’s 9144. Then, use the POWER() function to calculate the bits required. Use the argument POWER(2, X) where X is any positive value that will return an answer that’s greater than the MAX value. X, in this case, also represents the bits required. So for this example, the value of X is 14 which results in 16,384. Therefore, the column requires 14 bits of storage.

To reduce the required bits using Value Encoding, VertiPaq seeks out the MIN value in the column and subtracts it from each value. In this case, the MIN value in the column is 9003. If you subtract this from the column, it’ll return these values:

Using the same functions and arguments, you can see that for the new column, the MAX value is 141. And using 8 as the value of X results in 256. Therefore, the new column only requires 8 bits.

You can see how compressed the second is compared to the first column.

Once the data is compressed and you try to query the new column, the Storage Engine or Vertipaq scans this column. They won’t simply return the new values of the column. Instead, they add the subtracted value before returning the result back to the user.

However, Value Encoding only works on columns containing integers or values with fixed decimal numbers.

The second encoding method is called Run Length Encoding.

Run Length Encoding creates a data structure that contains the distinct value, a Start column, and a Count column.

Let’s have an example:

In this case, it identifies that one Red value is available in the first row. It then finds out that the Black value starts at the second row and is available for the next four cells. It proceeds to the third value, Blue, which starts at the sixth row and is available for the next three rows. And this goes on until it reaches the last value in the column.

So instead of storing the entire column, it creates a data structure that only contains information about where a particular value starts and where it ends, and how many duplicates it has.

For columns with the same structure, data can be further compressed by arranging the values in either ascending or descending order.

With this properly sorted column, you can see that the Run Length Encoding method now returns a data structure with one row less.

So if you’re dealing with many distinct values, it’s recommended to sort the column in the most optimal way possible. This will give you a data structure with lesser rows which in turn occupies lesser RAM.

Run Length Encoding can’t be applied to primary keys because primary key columns only contain unique values. So instead of storing one row for each value, it’ll store the column as it is.

The third encoding method is called Dictionary Encoding.

Dictionary Encoding creates a dictionary-like structure that contains the distinct value of a column. It also assigns an index to that unique value.

Using the previous example, let’s look at how Dictionary Encoding works. In this case, the values Red, Black, and Blue are assigned an index of 0, 1, and 2, respectively.

It then creates a data structure similar to that of Run Length Encoding. However, instead of storing the actual values, Dictionary Encoding stores the assigned index of each value.

This further reduces the RAM consumed because numbers take up lesser space than string values.

Dictionary Encoding also makes the tabular data type independent. That is, regardless if you have a column that can be stored in different data types, it won’t matter since the data structure will only store the index value.

However, even if it’s independent, the data type will still have an effect on the size of the dictionary. Depending on the data type you choose to save the column in, the dictionary (or data structure) size will fluctuate. But the size of the column itself will remain the same.

So depending on what data type you’ll choose, once Dictionary Encoding is applied on the column, Run Length Encoding can be applied afterward.

In this case, Analysis Services will create two data structures. It’ll first create a dictionary and then apply Run Length Encoding on it to further increase the compression of the column.

For the last part of this tutorial, let’s discuss how Analysis Services decides on the most optimal manner to sort data.

As an example, let’s look at a column containing Red, Blue, Black, Green, and Pink values. The numbers 1 to 5 have also been assigned to them. This acts as the dictionary of our column.

Now, fill an entire column in Excel with these values. Use this argument to generate a column containing these values at random.

Next, copy the entire column and paste it as a Value.

To reduce the amount of RAM consumed, you can sort the column from A to Z. If you check the size again, you can see that it’s been reduced to 12.5 MB.

The 1.9 MB reduction may not seem much. This is because the example used a single column in Excel to demonstrate. Excel is only limited to 1 million rows. However, in Power BI, your data can contain billions of rows and columns. The reduction in space used grow exponentially.

Once your data is sorted in the most optimal manner, Analysis Services applies either of the three compression techniques depending on the data type.

Doing so increases the compression of your data which greatly reduces the amount of memory consumed in your device. This makes your report more optimal making it easier to run and load.

Enterprise DNA Experts

Pneumonia Detection Using Cnn With Implementation In Python

Hey there! Just finished another deep learning project several hours ago, now I want to share what I actually did there. So the objective of this challenge is to determine whether a person suffers pneumonia or not. If yes, then determine whether it’s caused by bacteria or viruses. Well, I think this project should be called classification instead of detection.

Several x-ray images in the dataset used in this project.

In other words, this task is going to be a multiclass classification problem where the label names are: normal, virus, and bacteria. In order to solve this problem. I will use CNN (Convolutional Neural Network), thanks to its excellent ability to perform image classification. Not only that, but here I also implement the image augmentation technique as an approach to improve model performance. By the way, here I obtained 80% accuracy on test data which is pretty impressive to me.

The dataset used in this project can be downloaded from this Kaggle link. The size of the entire dataset itself is around 1 GB, so it might take a while to download. Or, we can also directly create a Kaggle Notebook and code the entire project there, so we don’t even need to download anything. Next, if you explore the dataset folder, you will see that there are 3 subfolders, namely train, test and val.

Well, I think those folder names are self-explanatory. In addition, the data in the train folder consists of 1341, 1345, and 2530 samples for normal, virus and bacteria class respectively. I think that’s all for the intro, let’s now jump into the code!

Note: I put the entire code used in this project at the end of this article.

Loading modules and train images

The very first thing to do when working with a computer vision project is to load all required modules and the image data itself. I use tqdm module to display the progress bar which you’ll see why it is useful later on. The last import I do here is ImageDataGenerator coming from the Keras module. This module is going to help us with implementing the image augmentation technique during the training process.

import os import cv2 import pickle import numpy as np import matplotlib.pyplot as plt import seaborn as sns from tqdm import tqdm from sklearn.preprocessing import OneHotEncoder from sklearn.metrics import confusion_matrix from keras.models import Model, load_model from keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten from keras.preprocessing.image import ImageDataGenerator

np.random.seed(22)

Next, I define two functions to load image data from each folder. The two functions below might look identical at glance, but there’s actually a little difference at the line with bold text. This is done because the filename structure in NORMAL and PNEUMONIA folders are slightly different. Despite the difference, the other process done by both functions is essentially the same. First, all images are going to be resized to 200 by 200 pixels large.

This is important to do since the images in all folders are having different dimensions while the neural networks can only accept data with a fixed array size. Next, basically all images are stored with 3 color channels, which is I think it’s just redundant for x-ray images. So the idea here is to convert all those color images to grayscale.

# Do not forget to include the last slash def load_normal(norm_path): norm_files = np.array(os.listdir(norm_path)) norm_labels = np.array(['normal']*len(norm_files)) norm_images = [] for image in tqdm(norm_files): image = cv2.imread(norm_path + image) image = cv2.resize(image, dsize=(200,200)) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) norm_images.append(image) norm_images = np.array(norm_images) return norm_images, norm_labels

def load_pneumonia(pneu_path): pneu_files = np.array(os.listdir(pneu_path)) pneu_labels = np.array([pneu_file.split('_')[1] for pneu_file in pneu_files]) pneu_images = [] for image in tqdm(pneu_files): image = cv2.imread(pneu_path + image) image = cv2.resize(image, dsize=(200,200)) image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) pneu_images.append(image) pneu_images = np.array(pneu_images) return pneu_images, pneu_labels

As the two functions above have been declared, now we can just use it to load train data. If you run the code below you’ll also see why I choose to implement tqdm module in this project.

norm_images, norm_labels = load_normal('/kaggle/input/chest-xray-pneumonia/chest_xray/train/NORMAL/')

pneu_images, pneu_labels = load_pneumonia('/kaggle/input/chest-xray-pneumonia/chest_xray/train/PNEUMONIA/')

The progress bar displayed using tqdm module.

Up to this point, we already got several arrays: norm_images, norm_labels, pneu_images, and pneu_labels. The one with _images suffix indicates that it contains the preprocessed images while the array with _labels suffix shows that it stores all ground truths (a.k.a. labels). In other words, both norm_images and pneu_images are going to be our X data while the rest is going to be y data. To make things look more straightforward, I concatenate the values of those arrays and store in X_train and y_train array.

X_train = np.append(norm_images, pneu_images, axis=0) y_train = np.append(norm_labels, pneu_labels)

The shape of the features (X) and labels (y).

By the way, I obtain the number of images of each class using the following code:

Finding out the number of unique values in our training set

Modern Encryption Technologies And Their Application

Cryptography in digital networking is a vital tool for protecting sensitive information. It shapes means of countering the illegal copying of intellectual property data.

Various encryption algorithms are used in the finance and business networks to protect against competitive intelligence.

All links and servers in such systems are secured, i.e., they are processed according to one or another encryption algorithm.

Interestingly, modern security software like Avira, Bitdefender, Norton has built-in cryptography components.

They ensure mandatory transmitting encryption of communication links at the network level. However, how do they work? Let’s find out!

Cryptography Objectives

One may consider cryptography as an essential tool for securing confidential data from:

Fraudulent activity.

Intentional violation of integrity or full erasing.

Unauthorized reading.

Unwanted copying.

The fundamental requirement for cryptographic protection is the principle of its equal strength.

Also read: Top 10 Helpful GitHub Storage For Web Developers

Principles of Use

There are several primary principles for applying cryptographic methods:

Encryption algorithms allow you to safely send data even if this happens in an unsafe environment (the Internet and

cloud encryption strategies

, as an example).

Encryption algorithms are used to protect files containing sensitive information to minimize the possibility of unauthorized access.

Encoding technologies are used not only to guarantee privacy but also to safeguard data integrity.

Cryptography is a means of verifying the credibility of data and sources (we are talking about digital signatures and certificates).

Algorithms, file formats, and key sizes may be freely available; however, the encryption method’s keys remain secret.

Cryptographic algorithms have made it possible to create a comprehensive information security system in large networks and information databases.

The significant reason is that they are grounded in the public key distribution. The attribute of public-key cryptosystems is that they are built based on asymmetric encryption algorithms.

This way, they use a much smaller number of keys for the exact number of users than a public key cryptosystem requires.

Today, there are many ready-made encryption algorithms with high cryptographic strength.

The encryptor has to generate its unique key to add the necessary cryptographic qualities to the data. Both encryption and decryption stages require using this key.

Encryption algorithms

Nowadays, numerous encryption algorithms have significant resistance to cryptanalysis (cryptographic strength). Three are three groups of encryption designs:

Hash function algorithms.

Asymmetric algorithms.

Symmetric algorithms.

Hashing is transforming an initial information array of random length into a fixed-length bit string.

There are many hash-function algorithms with different features like cryptographic strength, bit depth, computational complexity, etc.

Asymmetric systems are also named public-key cryptosystems. This is a data encryption method when the public key is shared over an open channel being not encrypted and used to verify an electronic signature and encrypt data.

A second private key is necessary to use to decrypt and create an electronic signature.

Symmetric encryption requires using an identical key for both encryption and decryption.

Also read: 10 Best Chrome Extensions For 2023

Certificates and their practical application

Certificates are generally used to exchange encrypted data over large networks. A public-key cryptosystem fixes the problem of sharing private keys between participants in a secure exchange.

However, it does not solve the problem of trusting public keys. There is a potential for an attacker to replace the public key and hijack the information encrypted with this key. The next action of the hacker will be decoding data using its own secret key

The idea of a certificate is to have a trusted third party. It involves two participants giving this actor information for safekeeping.

It is assumed that there are few such third parties, and all other users are aware of their public keys beforehand. Thus, a fraud of a third party public key is easily detected.

Certificate structure

The list of required and optional demands for a certificate is defined by the standards for its format (for example, PKCS12/PFX or DER). Usually, a certificate includes the following tags:

certificate duration (start and expiration date);

the name of its owner

information about the used encryption methods;

the number of public keys of the certificate owner;

name of certification authority;

the serial number of the certificate assigned by the certification authority;

a digital signature produced under a secret key method and backed by the authority granted by the owner

Certificate verification

The determination of the trust level to any user certificate usually derives from the certificate chain.

Moreover, its primary component is the certificate of the certification authority maintained in the user’s secure personal certificate storage.

The certificate chain verification procedure checks the link between the certificate owner’s name and its public key.

It assumes that all valid chains start with certificates granted by a single trusted certification authority.

Specific distribution and storage methods must be applied to ensure full trust in the public key of such a certificate.

Standardization in this sphere lets various applications interact using a single public key infrastructure.

Nathan Collier

I started out as a journalist, one of the first ones to ever write about cybersecurity. I’ve been writing about antiviruses for his entire life and working with some of the biggest names, helping them bring their products closer to the average users. Education: The University of Georgia, Print, Broadcast and Electronic Journalism. Working experience: 7 years in Georgia local news. Hobby: Baseball, cyber security

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