<p>In recent years, the advancement of smart healthcare has heightened concerns over medical information privacy. Reversible data hiding technology has been applied to medical images to ensure both images and patient data remain secure and tamper-free. In this paper, we propose a novel multi-party reversible data hiding scheme based on texture-guided hierarchical quantization coding for medical information security and privacy. The core of our approach is a novel Texture-Guided Hierarchical Quantization Coding (TGHQC) method, which can leverage the texture features of pixels to guide the hierarchical quantization coding process, thereby increasing the vacated room and enhancing embedding capacity. In addition, based on TGHQC, we further propose a multiple embedding method that not only supports embedding various types of data but also maximizes embedding efficiency while ensuring information security. Finally, we present a hierarchical sharing multi-party management framework that integrates TGHQC with the new embedding method, allowing hospitals to access patient privacy information and medical images only with explicit patient authorization. Our multi-party reversible hiding method effectively safeguards patient privacy and ensures secure storage and access to information within medical systems. Experimental results demonstrate the scheme’s superior embedding capacity and lossless reconstruction.</p>

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Multi-party reversible data hiding in shared medical images based on texture-guided hierarchical quantization coding

  • Wenbo Wan,
  • Haina Wang,
  • Lingchen Gu,
  • Yannan Ren,
  • Jing Li,
  • Lili Meng,
  • Jiande Sun

摘要

In recent years, the advancement of smart healthcare has heightened concerns over medical information privacy. Reversible data hiding technology has been applied to medical images to ensure both images and patient data remain secure and tamper-free. In this paper, we propose a novel multi-party reversible data hiding scheme based on texture-guided hierarchical quantization coding for medical information security and privacy. The core of our approach is a novel Texture-Guided Hierarchical Quantization Coding (TGHQC) method, which can leverage the texture features of pixels to guide the hierarchical quantization coding process, thereby increasing the vacated room and enhancing embedding capacity. In addition, based on TGHQC, we further propose a multiple embedding method that not only supports embedding various types of data but also maximizes embedding efficiency while ensuring information security. Finally, we present a hierarchical sharing multi-party management framework that integrates TGHQC with the new embedding method, allowing hospitals to access patient privacy information and medical images only with explicit patient authorization. Our multi-party reversible hiding method effectively safeguards patient privacy and ensures secure storage and access to information within medical systems. Experimental results demonstrate the scheme’s superior embedding capacity and lossless reconstruction.