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Internet privacy defines the huge range of technologies, protocols and concepts related to providing individual users or other parties more privacy protections in their use of the global Internet. Internet privacy takes several forms, such as mandatory privacy statements on websites, data sharing controls, data transparency initiatives, etc. Internet privacy is also called as online privacy.Internet privacy is a main problem today. Privacy over the Internet has enhanced some ethical problems, which is needed to be dealt with. While catering to the privacy requirements of the Internet users, it is needed to executing certain ethics.Privacy can define ambiguity in case ... Read More
The pandas Series.cumsum() method is used to find the cumulative sum of the elements in a series object.The Series.cumsum() method returns a cumulative sum with the same length as the original series object. The first element of the cumulative sum is the same as the input object.This method has three parameters which are “axis”, “skipna” and “args” keywords. The important parameter is “skipna” which is used to exclude Nan/null values by default, if we include the missing values then we need to set it to “False”.Example 1# importing required packages import pandas as pd import numpy as np # ... Read More
By using the cumprod() method in the Pandas Series constructor we can find out the cumulative products of the elements of a given series object.The cumprod() method returns a series of the same length as the original input series object, containing the cumulative product.And there are three parameters in the cumprod() method which are “axis”, “skipna” and additional keywords. The “skipna” parameter is used to exclude the missing values by default, if you want to include those missing values, then set the skipna parameter to “False”.Example 1# importing required packages import pandas as pd import numpy as np # ... Read More
The cummin() method in the Pandas Series constructor is used to find the cumulative minimum of the elements of a given series.The resultant cumulative minimum object has the same length as the original series object. The parameters of the cummin() method are “axis”, “skipna” and additional keywords.The “skipna” parameter excludes execution of missing values by default, if you want to execute those missing values too then set the skipna parameter to “False” then it includes Nan/null values also.Example 1# importing required packages import pandas as pd import numpy as np # create a pandas Series object series = pd.Series([9, ... Read More
Asymmetric encryption is a type of encryption that needs two separates yet mathematically associated keys to encrypt and decrypt information. The public key encrypts data while its correlating private key decrypts it.Asymmetric key authentication is a cryptographic approach that uses a different verification key in the CAD than the proving key utilized by the smart card. This method is usually executed using trapdoor one-way functions, in which smart card creates an electronic signature with its secret key and the CAD needs a public key to authenticate the signature.The Rivest-Shamir-Adelman (RSA) public key cryptosystem is the generally implemented asymmetric key authentication ... Read More
A symmetric key is one that can be used both to encrypt and decrypt data. This means that to decrypt data, one should have the similar key that was used to encrypt it. Symmetric encryption is usually more effective than asymmetric encryption and therefore preferred when high amounts of data need to be exchanged.It can be creating the shared key is complex using only symmetric encryption algorithms, so in some cases, an asymmetric encryption is used to create the shared key between two parties.The Digital Encryption Standard (DES) algorithm is a symmetric key cryptography approach commonly used smart card systems. ... Read More
There are several drawbacks of Kerberos which are as follows −Password guessing attacks − Password guessing attacks are not solved by Kerberos. If a user select a poor password, it is applicable for an attacker to successfully mount an offline dictionary attack by constantly attempting to decrypt messages acquired which are encrypted under a key changed from the user's password. The goal is on designing a user authentication protocol that is not affected to password guessing attacks. The main objective is to delete this password guessing attack.KDC spoofing − This define an attack which based essentially on the capability to ... Read More
The pandas Series.cummax() method is used to find the cumulative maximum of the elements in a series object.The length of the output series has the same length as the input series object. The output series is returned from the series.cummax() method which consists of a cumulative maximum whereas the first element remains the same.This method takes three parameters namely “axis”, “skipna” and additional keywords. The “skipna” parameter excludes Nan/null values by default, if we set it to “False” then it includes Nan/null values.Example 1# importing required packages import pandas as pd import numpy as np # create a pandas ... Read More
Dynamic Password is also known as One Time Password. It is used to solve the traditional problems which occur when the static Password authentication cannot cope with eavesdropping and replaying, making, guessing, etc.Using dynamic password, uncertainties will be treated in authentication information during the procedure of lodging to make authentication information different every time, which can enhance the security of information in the procedure of lodging. This technology can effectively prevent replay attack, and solve the issues that the static password is likely to be stolen in transmission and database.There are three fields which are transmitted to authentication server or ... Read More
The Series.cov() method in pandas is used to calculate the Covariance of the Series with the other Series by excluding null/NA or missing values.The covariance is a way of calculating the relationship between two random variables and it will tell us how much two random variables vary together.The output for this cov() method is a floating-point value that represents the covariance between two Series.This method has three parameters, which are other, min_period, and ddof.Example 1import pandas as pd import numpy as np # create pandas Series1 series1 = pd.Series([12, 34, 65, 21]) print("First series object:", series1) # create ... Read More