Integrated Multimodal Redaction Architecture With Reversible Cryptographic Obfuscation

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Author(s): Anshik Kumar Tiwari (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India)and Brindha Subburaj (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India)
Copyright: 2027
Pages: 22
Source title: Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques
Source Author(s)/Editor(s): Manisha Guduri (Lawrence Technological University, USA), George Pappas (Lawrence Technological University, USA)and Sandeep Thota (Oracle Inc., USA)
DOI: 10.4018/979-8-3373-7426-0.ch004

Keywords: Health Information Systems / Information Security & Privacy / Medical Information Science Reference / Security & Forensics

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Abstract

This work proposes a combined multimodal redaction framework that mitigates the fundamental drawbacks of traditional document anonymization methods. In healthcare, like many other domains such as legal, finance, and government sectors, traditional redaction relies heavily on manual processes that cause permanent data loss through irretrievable deletion of sensitive content. Once information is redacted using these conventional approaches, there is no way to recover it when access is required again, or it loses its utility in encrypted form. A patient's clinical report may contain personal information such as name, age, blood group, gender and even payment details. To address this, we propose an intelligent framework capable of handling diverse inputs: text in different formats, fonts, images with varying resolutions and aspect ratios; and mixed document types. The framework encrypts sensitive data using keys provided by one or more authorities. It is designed to manage the complexity of real-world medical documentation, while maintaining strong security guarantees and reversibility.

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Integrated Multimodal Redaction Architecture With Reversible Cryptographic Obfuscation

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