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.

个人和各种组织在使用Internet应用程序和移动设备时提供的数据对于生成解决方案和创造新机会非常有用。共享的数据需要精确才能获得质量结果。如果不应用某些隐私保护技术，可能会将包含个人敏感信息的数据泄露给全世界。隐私保护数据挖掘（PPDM）和隐私保护数据发布（PPDP）是可以用来保护隐私的一些技术。每种技术都有一些正面和负面的影响。缺点通常会造成数据丢失，数据使用率降低，数据多样性受损，安全性降低等。在本文中，作者提出了一种称为“重塑”的新技术。，与K-anonymity和K-means算法配合使用，可确保数据丢失最少，更好地保护隐私，同时保持数据的多样性。网络数据安全性也由该提议的模型处理。从理论上讲，在本研究论文中，我们表明了所提出的技术解决了上述所有缺点，并且还讨论了该技术的优缺点。