IJETE VOLUME 1, ISSUE 3 JULY – 2026
IJETEV1I3A001 - CRYPTOACOUSTIX: BLOCKCHAIN-DRIVEN AUDIO ENCRYPTION FRAMEWORK FOR DATA INTEGRITY AND CONFIDENTIALITY IN CLOUD SYSTEMS
AUTHORS : Muthusamy P, Sathiyapriya P, Deepika S, Priyadharshini L, Ramya C
ABSTRACT – In the modern era of digital transformation, cloud storage has become the backbone of data management, but it also introduces critical security challenges involving confidentiality, integrity, and authenticity. The system takes security a step further by embedding encryption keys and XOR-generated codes within audio files using the Least Significant Bit (LSB) steganography technique, concealing them from detection. This innovative approach not only safeguards sensitive data stored in the cloud but also leverages decentralized trust and imperceptible data hiding techniques to ensure resilience against modern cyber threats. These stego-audio files are then encrypted using Elliptic Curve Cryptography (ECC), providing multi-layered protection against brute-force and data interception attacks. The integration of AI and ML for dynamic threat analysis and intelligent key management, paving the way for a self-adaptive, intelligent, and highly secure cloud data protection ecosystem.
IJETEV1I3A002 - PRIVACY-PRESERVING MEDICAL IMAGE SHARING AND FEDERATION DISEASE DIAGNOSIS USING ECC WATERMARKING
AUTHORS : Arthi R, Sadhana S, Dinesh S, Hariharan R, Prasanth V
ABSTRACT – The rapid digitization of healthcare has increased the reliance on medical imaging for disease diagnosis. However, sharing sensitive medical images across scan centers, hospitals, and doctors poses significant privacy and security risks. In the current scenario, patient data and scan images are often transmitted or stored without strong identity protection, making them vulnerable to unauthorized access, tampering, and data breaches. Existing systems rely on traditional encryption of images or central storage of raw data, which can compromise privacy and violate regulatory compliance standards.Patient-controlled access is enforced by verifying the login ID against the watermark embedded in the scan image; ECC-secured federated learning, and controlled access mechanisms, the proposed system establishes a privacy-preserving, secure, and efficient framework for medical image sharing and distributed disease diagnosis
IJETEV1I3A003 - EDUMATE: A DUAL PURPOSE BROWSER EXTENSION FOR AI-DRIVEN LEARNING AND CYBERSECURITY PROTECTION
AUTHORS : Muthusamy P, Arthi R, Adithiya R, Kudiyarasan S, Logesh S
ABSTRACT – In today’s digital learning environment, students increasingly depend on online resources and AI tools for academic support. However, this growing reliance on the internet also exposes them to cybersecurity risks such as phishing, data theft, and malicious websites. EduMate provides four key modules: the Ask Module, which delivers step-by-step AI-generated answers to academic queries; the Summarize Module, which converts lengthy notes or articles into concise bullet points; and the URL Safety Module, which detects and warns users about suspicious or phishing links while browsing study materials. By merging AI-powered educational assistance with cybersecurity awareness, EduMate not only enhances students’ learning efficiency but also ensures a safer and more responsible digital learning experience. The project demonstrates the potential of integrating artificial intelligence with security awareness to build intelligent, ethical, and secure educational tools.