Quantum Secure Email Client Application Using Machine Learning

Savitri Nawade, Ubaid Kashif — Wed, 17 Jun 2026 00:00:00 +0000

The rapid advancement of quantum computing presents an existential threat to conventional cryptographic algorithms that underpin the security of modern digital communication systems, including widely deployed email encryption standards. This paper proposes a Quantum Secure Email Client Application that integrates Quantum Key Distribution (QKD) with machine learning techniques to deliver an unprecedented level of security for email communication against both classical and quantum computational attacks. The proposed system leverages the fundamental principles of quantum mechanics through the BB84 and E91 QKD protocols to generate and exchange encryption keys that are theoretically impervious to interception, as any eavesdropping attempt introduces detectable quantum state disturbances. Concurrently, machine learning algorithms perform real-time analysis of email traffic for anomaly detection, spam filtering, and user behaviour analysis, enabling adaptive and proactive threat identification. The system is implemented using Python with Flask backend integration, React.js frontend, and standard email protocol support (SMTP, IMAP, POP3) to ensure compatibility with existing email infrastructures. System architecture comprising a QKD Module, Machine Learning Engine, User Authentication Module, and Administrative Dashboard is designed and evaluated through comprehensive testing, including unit, integration, system, and user acceptance testing. All five critical test cases demonstrate successful execution, confirming the functional correctness and security effectiveness of the proposed framework. The results establish the Quantum Secure Email Client Application as a practical and future-proof solution for secure digital communication in the emerging quantum computing era.

A Comprehensive Review of Trust Governance, Explainable, and Sustainability Challenges in Edge-Driven Intelligent Systems

Mettu Paramesh, Joy Kumar — Tue, 16 Jun 2026 00:00:00 +0000

Edge-integrated intelligent systems have seen considerable progress in real-time distributed computing applications, and the current state of research, as presented in existing studies, shows considerable fragmentation in terms of trust governance, explainability, and sustainability. A systematic review of recent literature shows that close to 65% of edge AI research is concerned with performance and latency optimization, and less than 20% of the research includes formal trust governance or audit compliance. Similarly, although explainable artificial intelligence has seen considerable progress, more than 75% of the proposed solutions are cloud-centric. The sustainability studies are mostly concerned with hardware or network-level energy efficiency, and there is a lack of quantitative analysis of carbon-aware AI inference and lifecycle emissions in distributed edge systems. These findings point to the lack of comprehensive frameworks that combine governance-driven trust, lightweight explainable, and carbon-aware optimization. This review systematically points out the technological and architectural gaps and thus justifies the need for a trust-governed, explainable, and environmentally sustainable framework to facilitate the development of next-generation edge intelligence systems.

Journal of Cryptography and Network Security, Design and Codes

Quantum Secure Email Client Application Using Machine Learning

A Comprehensive Review of Trust Governance, Explainable, and Sustainability Challenges in Edge-Driven Intelligent Systems