Purpose: In the past, big data was the concern of governments and large commercial industries, and so these organizations had infrastructures separate from the public network to store and process their data. But today, big data is easily accessible to everyone through cloud infrastructure. In recent years, with the explosive development of the Internet, data storage and data processing technologies, privacy has been one of the major concerns in data mining Methodology: In this paper, after examining the dimensions and key components of flax data, privacy in big data; Blockchain solution for privacy protection is examined. Findings: This topic provides new opportunities for researchers in knowledge processing tasks. However, these opportunities often bring challenges as well. Originality/Value: Big data means a very large database that can be structured or unstructured. Big data is a database that is constantly getting bigger with the increase of information. Also with the rapid advancement of the Internet, data storage and data processing technologies, big data privacy has attracted a lot of attention. Before applying big data technology to mass applications and applications, a basic issue called privacy must be explored.

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.

In the last decades a huge number of information is produced per hour. This collected data can be used in some different fields such as business, healthcare, cybersecurity, after some process etc. in step two, the important process is that when this data is gathered, extraction of useful knowledge should be done from raw information. But the challenge that we face within this process, is the sensitivity of this information, which has made owners reluctant to share their sensitive information. This has led the study of the privacy of data in data mining to be a hot topic today. In this paper, an attempt is made to provide a framework for qualitative analysis of methods. This qualitative framework consists of three main sections: a comprehensive classification of proposed methods, proposed evaluation criteria, and their qualitative evaluation. In this case, we have a most important purpose of presenting this framework: 1) systematic introduction of the most important methods of privacy-preserving in data mining 2) creating a suitable platform for qualitative comparison of these methods 3) providing the possibility of selecting methods appropriate to the needs of application areas 4) systematic introduction of points Weakness of existing methods as a prerequisite for improving methods of PPDM.