PRIVACY-PRESERVATION METHODS IN BIG DATA ANALYTICS: A CASE STUDY OF TELECOMMUNICATION COMPANIES IN GHANA

The quantity of data obtained by numerous organizations about individuals is continuously rising. The big dimensional data are often included in data diversity sources. Mostly, data of this kind are stored in a table arrangement and involves data of raw content. Storing confidential data is a necessary task that needs a lot of awareness if the organizations associated with data are to allow the publication of data for different analyses and study purposes. Multiple attacks on privacy are connected with recording and attribute associated attacks. Many researchers and academicians have suggested preservation methods such as l-diversity, t-closeness, K-anonymity, etc. to protect the publication of the individual's private data. This paper mainly focused on Preservation Techniques in Big Data Analytics: A Case of Telecommunication companies in Ghana. The study employed multiple regression methods in analyzing the data, acquired through a closed questionnaire. The result revealed that all various Privacy Protection Methods used by Telecommunications in Ghana have an important contribution to the Protection of data privacy. The result also showed a strong and positive correlation between privacy-preservation and techniques employed in the study since the multiple regression results explained are over 95%, and the significant level is 0.09877. We recommend that more attention should be put on these techniques to improve the privacy terms and conditions.