Inner-product Functional Encryption with Fine-grained Revocation for Flexible EHR Sharing

• URL: http://arxiv.org/abs/2509.07804v1
• Date: Tue, 09 Sep 2025 14:44:40 GMT
• Title: Inner-product Functional Encryption with Fine-grained Revocation for Flexible EHR Sharing
• Authors: Yue Han, Jinguang Han, Liqun Chen, Chao Sun,
• Abstract summary: E-health record (EHR) contains a vast amount of continuously growing medical data and enables medical institutions to access patient health data conveniently.<n>Traditional public key encryption (PKE) can protect patient privacy, but cannot support flexible selective computation on encrypted EHR data.<n>We propose an inner-product functional encryption with fine-grained revocation (IPFE-FR) scheme, and then apply it to a flexible EHR sharing system.
• Score: 12.91562519654613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: E-health record (EHR) contains a vast amount of continuously growing medical data and enables medical institutions to access patient health data conveniently.This provides opportunities for medical data mining which has important applications in identifying high-risk patients and improving disease diagnosis, etc.Since EHR contains sensitive patient information, how to protect patient privacy and enable mining on EHR data is important and challenging.Traditional public key encryption (PKE) can protect patient privacy, but cannot support flexible selective computation on encrypted EHR data.Functional encryption (FE) allows authorised users to compute function values of encrypted data without releasing other information, hence supporting selective computation on encrypted data. Nevertheless, existing FE schemes do not support fine-grained revocation and update, so they are unsuitable for EHR system. In this paper,we first propose an inner-product functional encryption with fine-grained revocation (IPFE-FR) scheme, and then apply it to a flexible EHR sharing system. Our scheme possesses the following features:(1) a group manager can revoke a specific function computation of medical institutions on encrypted EHR data,instead of all function computation rights. (2) a revoked medical institution is not allowed to compute the function value of encrypted EHR data not only generated after the revocation, but also generated before the revocation. (3) secret keys issued to the same medical institution are bound together to prevent collusion attacks. The formal definition and security model of the IPFE-FR scheme are proposed.Furthermore, we present a concrete construction and reduce its security to the Learning with Errors (LWE) assumption which is quantum-resistant. Finally, the theoretical analysis and experimental implementation of our scheme are conducted to show its efficiency.

Related papers

• A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data [19.04813252998036]
  Homomorphic Encryption (HE) allows computations on encrypted data and enables aggregation of encrypted gradients without DP-induced noise.<n>We propose a Privacy-Preserving Machine Learning (PPML)-Hybrid method by introducing HE.<n>Our proposed method achieves comparable predictive accuracy while significantly reducing computation time relative to HE-only.
  arXiv  Detail & Related papers  (2025-11-08T16:18:42Z)
• Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems [89.35169042718739]
  collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data to cloud servers.<n>Recent studies have revealed that these intermediate features may not sufficiently preserve privacy, as information can be leaked and raw data can be reconstructed via model inversion attacks (MIAs)<n>This work first theoretically proves that the conditional entropy of inputs given intermediate features provides a guaranteed lower bound on the reconstruction mean square error (MSE) under any MIA.<n>Then, we derive a differentiable and solvable measure for bounding this conditional entropy based on the Gaussian mixture estimation and propose a conditional entropy algorithm to enhance the inversion robustness
  arXiv  Detail & Related papers  (2025-03-01T07:15:21Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• Noise-Resilient Homomorphic Encryption: A Framework for Secure Data Processing in Health care Domain [0.0]
  Homomorphic Integrity Model (HIM) is designed to enhance security, efficiency, and reliability in encrypted data processing.<n>De decryption mechanism ensures that the data recovered upon doing complex homomorphic computation will be valid and reliable.
  arXiv  Detail & Related papers  (2024-12-16T06:26:40Z)
• Confidential and Protected Disease Classifier using Fully Homomorphic Encryption [0.09424565541639365]
  Many users seek potential causes on platforms like ChatGPT or Bard before consulting a medical professional for their ailment. Despite the convenience of such platforms, sharing personal medical data online poses risks, including the presence of malicious platforms. We propose a novel framework combining FHE and Deep Learning for a secure and private diagnosis system.
  arXiv  Detail & Related papers  (2024-05-05T02:10:00Z)
• Retrieval Augmented Thought Process for Private Data Handling in Healthcare [53.89406286212502]
  We introduce the Retrieval-Augmented Thought Process (RATP) RATP formulates the thought generation of Large Language Models (LLMs) On a private dataset of electronic medical records, RATP achieves 35% additional accuracy compared to in-context retrieval-augmented generation for the question-answering task.
  arXiv  Detail & Related papers  (2024-02-12T17:17:50Z)
• A Solution for Commercializing, Decentralizing and Storing Electronic Medical Records by Integrating Proxy Re-Encryption, IPFS, and Blockchain [6.237350715303438]
  We propose an innovative solution for implementing a decentralized system utilizing an EVM-compatible blockchain and IPFS for decentralized storage. To ensure privacy and control, we employ Proxy Re-Encryption (PRE), a cryptographic authorized method, within the medical data marketplace. It empowers users with enhanced control over their health data through tamperproof blockchain smart contracts and IPFS, safeguarding the integrity and privacy of their medical records.
  arXiv  Detail & Related papers  (2024-02-08T09:09:03Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)
• NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training [64.54200987493573]
  We propose NeuraCrypt, a private encoding scheme based on random deep neural networks. NeuraCrypt encodes raw patient data using a randomly constructed neural network known only to the data-owner. We show that NeuraCrypt achieves competitive accuracy to non-private baselines on a variety of x-ray tasks.
  arXiv  Detail & Related papers  (2021-06-04T13:42:21Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)
• Cryptotree: fast and accurate predictions on encrypted structured data [0.0]
  Homomorphic Encryption (HE) is acknowledged for its ability to allow computation on encrypted data, where both the input and output are encrypted. We propose Cryptotree, a framework that enables the use of Random Forests (RF), a very powerful learning procedure compared to linear regression.
  arXiv  Detail & Related papers  (2020-06-15T11:48:01Z)
• DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction [67.91606509226132]
  Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. DeepEnroll is a cross-modal inference learning model to jointly encode enrollment criteria (tabular data) into a shared latent space for matching inference.
  arXiv  Detail & Related papers  (2020-01-22T17:51:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.