Multi-Privacy Collaborative Data publishing with Efficient Anonymization Techniques

Privacy-preserving in collaborative data publishing provides methods and tools for publishing the data while protecting the sensitive information in the data set. The success of data mining in privacy relies on the information sharing and quality of data in a distributed environment. Several anonymization techniques have been proposed such as bucketization, generalization which does not prevent membership disclosure and results in loss of information. Slicing used for high dimensional data and prevent membership disclosure but it takes place at only one column. The above techniques where each of them has taken the solution for creating a privacy when the microdata publishing. The system proposed in this paper tells the m-privacy and m-adversary technique with overlapping slicing concept which takes place more than two attribute column. M-privacy protects anonymized data from adversaries for a given privacy constraint. This technique shows the better utility and efficiency than the previous techniques.