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Amid the very hot issues, nowadays, the one related to Locations' privacy (GPS related) finds itself in top position. When it comes to talking about clients' locations in cloud or big data, the probable risk to privacy of clients' location is one of the major challenges should to be faced. In the recent years, a lot of developers and researchers have been paying attention to improve methods to provide privacy for data of clients' locations which it always processed by third-party. Big data could be like a puzzle for many researchers if they didn't understand it in the correct-side. Big data has to be understood as the process of gathering as much data as can be permitted in order to collect knowledge out of them (ideally in ground-breaking ways). so, this concept gives us attention that is privacy of clients' locations in cloud or big data could be under risks if it is used to collect knowledge or sell it to third-party. In our research, we try to show how we have implemented our algorithm (Diff-Anonym) in real data set (available at http://openaddresses.io) as to offer privacy for the clients' Locations in Big Data and cloud computing, as well as to improve our previous work which was simulation in normal data that appeared little differences in the results.

Privacy of data in subjects of cloud computing or big data is one of the most principal issues. The privacy methods studied in previous research showed that privacy infringement for cloud computing or big data happened because multi risks on data by external or internal attackers. An important risk to take into consideration when speaking of the privacy of the stored transactions is represented by the transactions' information which is not in the owner's control. Such a case is represented by the cloud servers that are administered by cloud providers which cannot be wholly trusted by the users with sensitive, private data such as business plans or private information. A simple method for protecting data privacy is by applying certain privacy techniques onto transactions' data, followed by the upload of the modified data into the cloud. In this paper, we are proposing a case study that is built on levels containing three models: cloud's architecture, transaction's manager and clients. Moreover, we consider that our case study is based on the premise of zero trust among the three models, therefore all the transactions take place with third-parties and the data movements are realized going through various levels of security.

The main issue with big data in cloud is the processed or used always need to be by third party. It is very important for the owners of data or clients to trust and to have the guarantee of privacy for the information stored in cloud or analyzed as big data. The privacy models studied in previous research showed that privacy infringement for big data happened because of limitation, privacy guarantee rate or dissemination of accurate data which is obtainable in the data set. In addition, there are various privacy models. In order to determine the best and the most appropriate model to be applied in the future, which also guarantees big data privacy, it is necessary to invest in research and study. In the next part, we surfed some of the privacy models in order to determine the advantages and disadvantages of each model in privacy assurance for big data in cloud. The present study also proposes combined Diff-Anonym algorithm (K-anonymity and differential models) to provide data anonymity with guarantee to keep balance between ambiguity of private data and clarity of general data.