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

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posted on 17.08.2021, 17:32 by Syed Usama Khalid BukhariSyed Usama Khalid Bukhari, Anum Qureshi, Adeel Anjum, Munam Ali Shah

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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Email Address of Submitting Author

syedusama@gmail.com

ORCID of Submitting Author

0000-0003-1581-8609

Submitting Author's Institution

University of Derby

Submitting Author's Country

United Kingdom

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