In this article, one of the most important methods of steganography on VoIP called echo hiding is improved. This method has advantages in maintaining the statistical and perceptual characteristics of audio signals as well as security against the sensitivity of human audio system (HAS). However, it has lots of errors in detecting coded and hidden messages, which is detectable using existing steganalysis methods. The percentage of extracting messages in these improved methods of echo hiding is high, but they lower the security of the method. In this article, a method is presented to improve the method of extracting echo hiding, and enhance its security through a combined method based on spread spectrum. To improve the extraction, a wrong hypothesis is corrected and substituted. To improve security using a pseudo-random key generation algorithm, spread spectrum and echo hiding methods are used randomly. To evaluate the proposed extraction, numerous extraction tests are carried out in the normal state and in the event of attacks. A steganalyser has also been used to assess security improvements. The results gained through different experiments on the security of steganography indicate a 3-percent increase in steganalysis errors. The proposed extraction method was modified based on the main method and resulted in more than 10% improvement.

In this paper, echo cancellation is done using genetic algorithm (GA). The genetic algorithm is implemented by two kinds of crossovers, heuristic and microbial. A new procedure is proposed to estimate the coefficients of adaptive filters used in echo cancellation with combination of the GA with Least-Mean-Square (LMS) method. The results are compared for various values of LMS step size and different types of crossovers which are all satisfactory. Reverse SNR is used as the fitness function. It can estimate an echo path with definite length of impulse response with an adaptive filter with desired length.Results show that the proposed combined GA-LMS method operates more satisfactory than simple GA in terms of the number of generations needed to achieve a particular amount of echo cancellation. Different tests show that GAs running with heuristic crossover converge faster than GAs with microbial crossover. Results are also compared with LMS algorithm. Although LMS is faster, but its solutions are less precise and it diverges in some cases. But our proposed method always converges.

Association rules are among important techniques in data mining which are used for extracting hidden patterns and knowledge in large volumes of data. Association rules help individuals and organizations take strategic decisions and improve their business processes. Extracted association rules from a database contain important and confidential information that if published, the privacy of individuals may be threatened. Therefore, the process of hiding sensitive association rules should be performed prior to sharing the database. This is done through changing the database transactions. These changes must be made in such a way that all sensitive association rules are hidden and a maximum number of non-sensitive association rules are extractable from the sanitized database. In fact, a balance is to be established between hiding the sensitive rules and extracting the non-sensitive rules. A new algorithm is presented in this paper to create a balance between preserving privacy and extracting knowledge. The items of sensitive rules are clustered in the proposed algorithm, in order to reduce changes. In fact, reduction of changes and clustering of rules are applied in order to reduce the side effects of the hiding process on non-sensitive rules.

Data repositories contain sensitive information which must be protected from unauthorized access. Existing data mining techniques can be considered as a privacy threat to sensitive data. Association rule mining is one of the utmost data mining techniques which tries to cover relationships between seemingly unrelated data in a data base.. Association rule hiding is a research area in privacy preserving data mining (PPDM) which addresses a solution for hiding sensitive rules within the data problem. Many researches have be done in this area, but most of them focus on reducing undesired side effect of deleting sensitive association rules in static databases. However, in the age of big data, we confront with dynamic data bases with new data entrance at any time. So, most of existing techniques would not be practical and must be updated in order to be appropriate for these huge volume data bases. In this paper, data anonymization technique is used for association rule hiding, while parallelization and scalability features are also embedded in the proposed model, in order to speed up big data mining process. In this way, instead of removing some instances of an existing important association rule, generalization is used to anonymize items in appropriate level. So, if necessary, we can update important association rules based on the new data entrances. We have conducted some experiments using three datasets in order to evaluate performance of the proposed model in comparison with Max-Min2 and HSCRIL. Experimental results show that the information loss of the proposed model is less than existing researches in this area and this model can be executed in a parallel manner for less execution time.