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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.03508 |
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| _version_ | 1866911361956577280 |
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| author | Cui, Zhuohan Lang, Qianqian Song, Zikun |
| author_facet | Cui, Zhuohan Lang, Qianqian Song, Zikun |
| contents | This paper critically examines the 2022 Medibank health insurance data breach, which exposed sensitive medical records of 9.7 million individuals due to unencrypted storage, centralized access, and the absence of privacy-preserving analytics. To address these vulnerabilities, we propose an entropy-aware differential privacy (DP) framework that integrates Laplace and Gaussian mechanisms with adaptive budget allocation. The design incorporates TLS-encrypted database access, field-level mechanism selection, and smooth sensitivity models to mitigate re-identification risks. Experimental validation was conducted using synthetic Medibank datasets (N = 131,000) with entropy-calibrated DP mechanisms, where high-entropy attributes received stronger noise injection. Results demonstrate a 90.3% reduction in re-identification probability while maintaining analytical utility loss below 24%. The framework further aligns with GDPR Article 32 and Australian Privacy Principle 11.1, ensuring regulatory compliance. By combining rigorous privacy guarantees with practical usability, this work contributes a scalable and technically feasible solution for healthcare data protection, offering a pathway toward resilient, trustworthy, and regulation-ready medical analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03508 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Critical Analysis of the Medibank Health Data Breach and Differential Privacy Solutions Cui, Zhuohan Lang, Qianqian Song, Zikun Cryptography and Security This paper critically examines the 2022 Medibank health insurance data breach, which exposed sensitive medical records of 9.7 million individuals due to unencrypted storage, centralized access, and the absence of privacy-preserving analytics. To address these vulnerabilities, we propose an entropy-aware differential privacy (DP) framework that integrates Laplace and Gaussian mechanisms with adaptive budget allocation. The design incorporates TLS-encrypted database access, field-level mechanism selection, and smooth sensitivity models to mitigate re-identification risks. Experimental validation was conducted using synthetic Medibank datasets (N = 131,000) with entropy-calibrated DP mechanisms, where high-entropy attributes received stronger noise injection. Results demonstrate a 90.3% reduction in re-identification probability while maintaining analytical utility loss below 24%. The framework further aligns with GDPR Article 32 and Australian Privacy Principle 11.1, ensuring regulatory compliance. By combining rigorous privacy guarantees with practical usability, this work contributes a scalable and technically feasible solution for healthcare data protection, offering a pathway toward resilient, trustworthy, and regulation-ready medical analytics. |
| title | A Critical Analysis of the Medibank Health Data Breach and Differential Privacy Solutions |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2601.03508 |