A Non-Reversible Privacy Preservation Model for Outsourced High-Dimensional Healthcare Data
2021
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.
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