Keywords : Cryptography

A Method for Randomly Hiding Secret Encrypted Data in Images using Cellular Automata

Ansam O. Abdulmajeed; Fatima M. Abdullatif; Wafaa A. Mustafa

AL-Rafidain Journal of Computer Sciences and Mathematics, 2019, Volume 13, Issue 1, Pages 28-36
DOI: 10.33899/csmj.2019.163507

The present research was aimed to design and implement an algorithm that combines cryptography and steganography to achieve a higher level of security. The algorithm hid encrypted text into color images in a scattered manner based on randomly generated numbers. The rules of cellular automata were used to encrypt the secret text and generate random numbers. Each character of a secret text was treated as a cellular automaton and the rule 153 was used to encrypt it with the help of a secret key. The encrypted text was hide in the red plane of the cove image after generating random numbers by applying rules 30, 60, 90 as a hybrid cellular automata using the same secret key as an initial seed. The results were tested on a number of images that shown that the algorithm hide the secret text in the images without distorting it clearly, the algorithm also retrieved the entire secret text without any loss. It was concluded that the use of hybrid cellular automata is better in generating random numbers than using uniform cellular automata.  It was also concluded that applying the retrieval algorithm with any minor changes in the value of the secret key affects the output of the decryption and the output of random number generation and causes a significant difference in the retrieved text.

Hiding Sensitive Frequent Itemsets over Privacy Preserving Distributed Data

Alaa Jumaa; Sufyan T. F. Al-Janabi; Nazar A. Ali

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 1, Pages 91-105
DOI: 10.33899/csmj.2013.163427

Data mining is the process of extracting hidden patterns from data. One of the most important activities in data mining is the association rule mining and the new head for data mining research area is privacy of mining. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Data mining can be applied on centered or distributed databases. Most efficient approaches for mining distributed databases suppose that all of the data at each site can be shared.  Privacy concerns may prevent the sites from directly sharing the data, and some types of information about the data. Privacy Preserving Data Mining (PPDM) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes.
In this paper, the problem of privacy preserving association rule mining in horizontally distributed database is addressed by proposing a system to compute a global frequent itemsets or association rules from different sites without disclosing individual transactions. Indeed, a new algorithm is proposed to hide sensitive frequent itemsets or sensitive association rules from the global frequent itemsets by hiding them from each site individually. This can be done by modifying the original database for each site in order to decrease the support for each sensitive itemset or association rule.  Experimental results show that the proposed algorithm hides rules in a distributed system with the good execution time, and with limited side effects. Also, the proposed system has the capability to calculate the global frequent itemsets from different sites and preserves the privacy for each site.

Design and Implementation of Stream Cipher Using Neural Network

Siddeq Y. Ameen; Mazin Z. Othman; Safwan Hasoon; Moyed Abud Al-Razaq

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 1, Pages 237-249
DOI: 10.33899/csmj.2009.163781

The centaral problem in stream cipher cryptograph is the  the difficulty to generate a long unpredicatable sequence of binary signals from short and random key. Unpredicatable sequence are desirable in cryptography because it is impossible, given a reasonable segment of its signals and computer resources, to find out more about them. Pseudorandom bit generators have been widely used to construct these sequences.
The paper presents a PN sequence generator that uses neural network. Computer simulation tests have been carried out to check  the  randomness  of the generated  through statistical tests. There tests have shown the successful PN sequence generator passes all the recommended tests. The paper also proposes and validates the data encryption and decryption process using neural network instead of using traditional methods (Exclusive or). This task increases the difficulty in the breaking the cipher.

Application of Polyalphabetic Substitution Cipher using Genetic Algorithm

Ghusoon Salim Basheer

AL-Rafidain Journal of Computer Sciences and Mathematics, 2008, Volume 5, Issue 1, Pages 57-68
DOI: 10.33899/csmj.2008.163949

Several Genetic Algorithms have been developed for applications of cryptography problem; the primary distinction among all of them being the G.A. used for decryption problem and obtains the plain text. In this paper a new approach is proposed using Genetic Algorithm with cryptography. G.A. is used to obtain a best secret key in polyalphabetic substitution cipher. This key will be used then for encryption and decryption with a high level of security. The program is written in Matlab language (6.5).

Cryptanalysis of Knapsack Cipher Using Genetic Algorithm

Subhi H. Hamdon; Najlaa B. Al-Dabbagh; Milad J. Saeed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2007, Volume 4, Issue 2, Pages 125-136
DOI: 10.33899/csmj.2007.164031

This research offers a new method in Cryptanalysis of knapsack cipher. It focuses on the application of genetic algorithm as a modern way in solving complex problems (problems have a huge numbers of alternate solutions in appropriate time). One of these problems is knapsack problem which is considered one of the known problems in operation researches. Cryptanalysis is done by using a new algorithm that is different from known knapsack breaking algorithm. Genetic algorithm has recently been successfully applied to the cryptanalysis of ciphers, among them Substitution  ciphers and Transposition ciphers. This research deals with another type of ciphers called Public-key ciphers, that are high secure ciphers because they are based on NP-Complete problems.