Privacy preservation of high-dimensional healthcare data is an emerging
problem. Privacy breaches are becoming more common than before and
affecting thousands of people. Every individual has sensitive and
personal information which needs protection and security. Uploading and
storing data directly to the cloud without taking any precautions can
lead to serious privacy breaches. It's a serious struggle to publish a
large amount of sensitive data while minimizing privacy concerns. This
leads us to make crucial decisions for the privacy of outsourced
high-dimensional healthcare data. Many types of privacy preservation
techniques have been presented to secure high-dimensional data while
keeping its utility and privacy at the same time but every technique has
its pros and cons. In this paper, a novel privacy preservation NRPP
model for high-dimensional data is proposed. The model uses a
privacy-preserving generative technique for releasing sensitive data,
which is deferentially private. The contribution of this paper is
twofold. First, a state-of-the-art anonymization model for
high-dimensional healthcare data is proposed using a generative
technique. Second, achieved privacy is evaluated using the concept of
differential privacy. The experiment shows that the proposed model
performs better in terms of utility.