International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
Al-Enhanced Privacy-Preserving EMR Search using Natural Language Query Translation and Intelligent Revocation Uday Kiran .M 1, Eshwar .P 2, Sampath .S 3, Saketh .P4 1234Department of Information Technology, TKR College of Engineering and Technology, Telangana, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The rapid digitization of healthcare has led to a
access. Healthcare data breaches have become a serious concern due to unauthorized access, insider misuse, and weak access revocation mechanisms in conventional EMR systems.
significant increase in Electronic Medical Records (EMRs), which contain highly sensitive patient information such as diagnoses, prescriptions, lab reports, and treatment history. While digital storage improves accessibility and clinical efficiency, it introduces major privacy and security challenges, especially when searching medical records stored in encrypted form. Traditional EMR systems either store records in plaintext or require full decryption during search, which increases the risk of data exposure. Moreover, conventional keyword-based retrieval lacks semantic understanding, resulting in low accuracy and poor relevance. This paper proposes an AI-enhanced privacy-preserving EMR search system that enables secure and intelligent retrieval of encrypted medical data. The system integrates Fernet AES encryption for EMR confidentiality, SHA-256 hashed keyword indexing for secure keyword lookup, and Sentence Transformer embeddings for semantic search. A hybrid query engine combines hashed keyword matching with embeddingbased similarity ranking to improve retrieval accuracy. The framework also enforces strong security through Role-Based Access Control (RBAC), patient-ID restricted access, OTP-based authentication, and dynamic access revocation through key invalidation. A Stream lit-based dashboard provides rolespecific interfaces for Admin, Doctor, Nurse, and Patient. Experimental results show that the proposed approach supports natural-language EMR search while preserving confidentiality, preventing unauthorized access, and ensuring clinically relevant results. The system demonstrates that secure searchable EMR platforms can achieve both high privacy and high usability for modern healthcare environments.
1.1 Need for Secure EMR Storage Traditional EMR platforms often rely on centralized database storage with basic encryption and authentication. However, these systems are vulnerable because encrypted records are usually decrypted during retrieval and search operations, which increases exposure risks. Searchable encryption has been proposed as an effective approach to allow retrieval without revealing plaintext medical content [1]. Therefore, strong cryptographic mechanisms are required to ensure confidentiality and tamper resistance.
1.2 Challenges in Searching Encrypted Medical Records Performing search operations over encrypted EMRs is technically difficult because encryption hides the semantic meaning and structure of the stored data. Most existing systems require decryption before performing keyword search, which defeats the purpose of encryption. Privacypreserving search techniques attempt to solve this issue by allowing search functionality while maintaining confidentiality [1]. However, keyword-only search still produces low accuracy and high false positives in clinical settings.
1.3 Role of NLP and Semantic Search
Key words : Electronic Medical Records, PrivacyPreserving Search, Searchable Encryption, Semantic Search, NLP, Sentence Transformers, RBAC, Fernet AES, SHA-256, Access Revocation.
Natural Language Processing (NLP) has become a powerful tool for enabling intelligent retrieval of medical documents. Semantic search techniques allow the system to understand query intent and retrieve clinically relevant records even when the exact keyword is not present. Studies have shown that semantic retrieval using NLP-based embeddings significantly improves EMR search relevance [2]. Sentence Transformer models are widely used for generating semantic embeddings for text-based similarity matching [5].
1. INTRODUCTION The rapid growth of digital healthcare has significantly increased the adoption of Electronic Medical Records (EMRs) across hospitals, clinics, and diagnostic centres. EMRs contain highly sensitive patient data such as medical history, diagnoses, prescriptions, laboratory reports, and treatment plans. While digitization improves clinical efficiency and accessibility, it also introduces major challenges related to data privacy, security, and controlled
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1.4 Importance of Fine-Grained Access Control In healthcare, not all users should access all records. Role-Based Access Control (RBAC) is a widely accepted
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