Conceptual Model Suggestions for Privacy Preserving Big Data Publishing

Recent developments in IT has increased the speed of data production and processing, as a result, big data concept with components such as volume, velocity, variety and value has emerged. In order to get more benefit from big data, it is necessary to share or publish the data by preserving or respecting privacy. The literature reviews report that there is no model that facilitates publishing big data by preserving privacy. Designing Privacy Preserving Big Data Publishing (PPBDP) models is important to direct all the parties and to meet the requirements of them correctly, and to create the right infrastructures and services. In addition, it is necessary to consider some factors such as cost and security when designing these models. In this study, privacy preserving data publishing models were reviewed, compared based on various criteria and then evaluated based on privacy risk levels. Finally, big data architecture based new conceptual models were then established for the first time according to these evaluations and privacy risk levels. It is expected that the proposed models might contribute to the literature on some issues, such as publishing big data with preserving privacy, minimizing privacy risks and obtaining maximum benefit from the big data.