MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641081041 A) filed by Dr. R. Naresh; Dr. C. N. S. Vinothkumar; Dr. A. Saranya; Dr. M. Mageshwari; Mrs. S. Deeba; Mrs. D. Sudha; Mrs. R. Ranjani; Mr. U. Sundhar; Ms. Chitra Devi P; and Mr. R. Jayaraman on July 01, 2026, for Secure Cloud-Based E-Health System With Multi-Factor Authentication And Data Integrity Verification.

Inventors include Dr. R. Naresh; Dr. C. N. S. Vinothkumar; Dr. A. Saranya; Dr. M. Mageshwari; Mrs. S. Deeba; Mrs. D. Sudha; Mrs. R. Ranjani; Mr. U. Sundhar; Ms. Chitra Devi P; and Mr. R. Jayaraman.

The application for the patent was published on July 03, 2026, under issue no. 27/2026.

Abstract: In the present invention, a novel Multi-Factor Authentication and Adaptive Data Integrity Verification Algorithm with Machine Learning (MFA-ADIV-ML) is developed, which is integrated with a Cloud-Based e-Health Monitoring System to provide secure data management in healthcare and intelligent patient monitoring. It establishes adaptive and secure user authentication through the combination of password authentication, biometric verification, One-Time Password (OTP) validation, device fingerprinting and machine learning based trust evaluation. Patient data and medical equipment information is securely encrypted and stored in the cloud, and an adaptive integrity verification mechanism ensures data authenticity and security from unauthorized changes through cryptographic hashing, digital signatures, and anomaly detection. The machine learning model continuously monitors patient health parameters, cloud transactions, access patterns, and user behavior, identifying potential security risks, forecasting health issues, and issuing immediate alerts. The proposed system also enables role-based access control, secure telemedicine, audit trails, and interoperability in healthcare services. The novelty of the invention is that it combines adaptive multi-factor authentication, intelligent data integrity verification, and machine learning based health and security analytics all in a single cloud-based e-health system to provide enhanced data security, data privacy, reliability and support for healthcare decision making.

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