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LBSs are Location-Based Services that provide certain service based on the current or past user's location. During the past decade, LBSs have become more popular as a result of the widespread use of mobile devices with position functions. Location information is a secondary information that can provide personal insight about one's life. This issue associated with sharing of data in cloud-based locations. For example, a hospital is a public space and the actual location of the hospital does not carry any sensitive information. However, it may become sensitive if the specialty of the hospital is analyzed. In this paper we proposed design presents a combination of methods for providing data privacy protection for location-based services (LBSs) with the use of cloud service. The work built in zero trust and we start to manage the access to the system through different levels. The proposal is based on a model that stores user location data in supplementary servers and not in non-trustable third-party applications. The approach of the present research is to analyze the privacy protection possibilities through data partitioning. The data collected from the different recourses are distributed into different servers according to the partitioning model based on multi-level policy. Access is granted to third party applications only to designated servers and the privacy of the user profile is also ensured in each server, as they are not trustable.

Modern technology and rapid progress in the development of systems and electronic devices have created a significant gap in protecting the privacy of individuals and communities. Intrusion into clients’ privacy in the past was mostly done through software errors or poor protection of the systems. However, intrusion into privacy in the last few years is mostly due to disclosure of information by customers directly or indirectly. Usually, information separately is not important, but if it can be collected together, it can produce new information that is in breach of privacy. Concerns among individuals or communities about privacy violations have encouraged us to conduct a survey to identify the individual information that generates new information causing privacy violations. This paper focuses on identifying whether the most harm is done by disclosing general information or location information. Another aspect followed by the study is to identify if there are discrepancies in the perception of individuals about their willingness to disclose the information if the data are optional.

In computing, one of the rapidly growing technologies is cloud computing, which comes with great benefits, as well as with some drawbacks. One of the issues examined in the present paper is the secure clients' location sharing method, from the perspective of data integrity and authenticity. These are issues associated with sharing of data in cloud-based locations. The first aspect to analyze is the access mechanism, which raises security issues if it is not secured enough for sensitive data. Data stored in cloud (big data) is often accessed by third party, which increases the exposure to attacks. The privacy violation risks thus become higher. Cloud-stored data usually is processed or used by third party. Data owners (clients) must ensure information privacy of the stored or processed data. The solution proposed in this paper for cloud data management and classification is a schema organized on multiple levels and based on policy in order to increase reliability of cloud computing in terms of confidentiality of sensitive data. The paper is based on a solid security policy structure which follow the specifications and abilities. The algorithm presented in the paper efficiently provides data accuracy and validity with the employment and management of the outlined policies, complying to cloud customers' specifications and cloud providers' possibilities.

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.