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<p>Intelligent medical service significantly improves healthcare quality and convenience but raises serious concerns regarding data security and patient privacy. Ensuring secure diagnostics and protecting sensitive medical data remain key challenges in the adoption of IoT-based healthcare systems. To tackle these challenges, we introduce a Policy-Hiding Attribute-based Inner Product Functional Encryption (PH-AIPFE) scheme specifically designed for achieving fine-grained and privacy-preserving access control mechanisms in medical IoT environments. Unlike traditional encryption schemes that output plaintext upon successful decryption, our scheme functions as a true functional encryption system–it outputs the inner product value <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44443_2025_338_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="45" /> </InlineMediaObject> <EquationSource Format="TEX">\(\langle \vec {o}, \vec {u} \rangle \)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">⟨</mo> <mover accent="true"> <mi>o</mi> <mo stretchy="false">→</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>u</mi> <mo stretchy="false">→</mo> </mover> <mo stretchy="false">⟩</mo> </mrow> </math></EquationSource> </InlineEquation> between the access policy vector and user attribute vector when access policies are satisfied. This enables privacy-preserving medical data analysis while concealing both access structures and user attributes. By leveraging blind vectors, PH-AIPFE prevents unauthorized inference of sensitive access structures while supporting practical inner product computations for medical applications. We provide a formal security analysis proving that PH-AIPFE achieves IND-pre-CPA security under the standard model. Experimental results demonstrate the scheme’s computational efficiency and scalability compared to existing approaches, making it suitable for protecting sensitive health information in intelligent medical IoT applications.</p>

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Policy-hiding inner product encryption for fine-grained access control in intelligent medical service

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月6日

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  • Yanting Ye,
  • Changgen Peng,
  • Weijie Tan

摘要

Intelligent medical service significantly improves healthcare quality and convenience but raises serious concerns regarding data security and patient privacy. Ensuring secure diagnostics and protecting sensitive medical data remain key challenges in the adoption of IoT-based healthcare systems. To tackle these challenges, we introduce a Policy-Hiding Attribute-based Inner Product Functional Encryption (PH-AIPFE) scheme specifically designed for achieving fine-grained and privacy-preserving access control mechanisms in medical IoT environments. Unlike traditional encryption schemes that output plaintext upon successful decryption, our scheme functions as a true functional encryption system–it outputs the inner product value \(\langle \vec {o}, \vec {u} \rangle \) ⟨ o → , u → ⟩ between the access policy vector and user attribute vector when access policies are satisfied. This enables privacy-preserving medical data analysis while concealing both access structures and user attributes. By leveraging blind vectors, PH-AIPFE prevents unauthorized inference of sensitive access structures while supporting practical inner product computations for medical applications. We provide a formal security analysis proving that PH-AIPFE achieves IND-pre-CPA security under the standard model. Experimental results demonstrate the scheme’s computational efficiency and scalability compared to existing approaches, making it suitable for protecting sensitive health information in intelligent medical IoT applications.