A Comparative Survey on Privacy Preservation and Privacy Measuring Techniques in Data Publishing

In recent years, many enterprises produce big amount of data with active/passive integration of modern data generating tools. These data consists of some important and sensitive information about any individual so security of these dataset is a big concern. There are some techniques has been already introduced like generalization, suppression, slicing, overlapping slicing integrated with k-anonymity, l-diversity, t-closeness and differential privacy. In this paper a survey has been done, which draw an attention towards the existing techniques with comparison based on different parameters and it has also discussed that what are the privacy measure/measurement of privacy and which technique will should we use that we will get a privacy preserved dataset.