Remodeling: improved privacy preserving data mining (PPDM)

Abstract

The data provided by individuals and various organizations while using internet applications and mobile devices are very useful to generate solutions and create new opportunities. The data which is shared needs to be precise to get the quality results. The data which may contain an individual’s sensitive information cannot be revealed to the world without applying some privacy preserving technique on it. Privacy preserving data mining (PPDM) and Privacy preserving data publishing (PPDP) are some of the techniques which can be utilized to preserve privacy. There are some positives and negatives for every technique. The cons frequently constitute loss of data, reduction in the utility of data, compromised diversity of data, reduced security, etc. In this paper, the authors propose a new technique called Remodeling, which works in conjunction with the k-anonymity and K-means algorithm to ensure minimum data loss, better privacy preservation while maintaining the diversity of data. Network data security is also handled by this proposed model. In this research paper, theoretically, we have shown that the proposed technique addresses all the above-mentioned cons and also discusses the merits and demerits of the same.