Quantum Cryptography - Protocols and Security Analysis: Analyzing quantum cryptographic protocols and conducting security analysis to assess their resistance against classical and quantum attacks
Keywords:
Quantum cryptographyAbstract
Quantum cryptography offers a revolutionary approach to secure communication, leveraging the principles of quantum mechanics to establish cryptographic keys with unparalleled security. This paper provides an in-depth analysis of quantum cryptographic protocols, focusing on their design, implementation, and security features. We evaluate the security of these protocols against both classical and quantum attacks, highlighting their strengths and limitations. Through a comprehensive review of existing literature and case studies, we aim to provide insights into the practical implications of quantum cryptography for secure communication in the era of quantum computing.
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References
Tatineni, Sumanth, and Anirudh Mustyala. "Advanced AI Techniques for Real-Time Anomaly Detection and Incident Response in DevOps Environments: Ensuring Robust Security and Compliance." Journal of Computational Intelligence and Robotics 2.1 (2022): 88-121.
Biswas, A., and W. Talukdar. “Robustness of Structured Data Extraction from In-Plane Rotated Documents Using Multi-Modal Large Language Models (LLM)”. Journal of Artificial Intelligence Research, vol. 4, no. 1, Mar. 2024, pp. 176-95, https://thesciencebrigade.com/JAIR/article/view/219.
Bojja, Giridhar Reddy, and Jun Liu. "Impact of it investment on hospital performance: a longitudinal data analysis." (2020).
Vemoori, Vamsi. "Towards a Driverless Future: A Multi-Pronged Approach to Enabling Widespread Adoption of Autonomous Vehicles-Infrastructure Development, Regulatory Frameworks, and Public Acceptance Strategies." Blockchain Technology and Distributed Systems 2.2 (2022): 35-59.
Tillu, Ravish, Muthukrishnan Muthusubramanian, and Vathsala Periyasamy. "Transforming regulatory reporting with AI/ML: strategies for compliance and efficiency." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.1 (2023): 145-157.
Bayani, Samir Vinayak, Ravish Tillu, and Jawaharbabu Jeyaraman. "Streamlining Compliance: Orchestrating Automated Checks for Cloud-based AI/ML Workflows." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.3 (2023): 413-435.
Tomar, Manish, and Vathsala Periyasamy. "Leveraging advanced analytics for reference data analysis in finance." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.1 (2023): 128-136.
Abouelyazid, Mahmoud. "Advanced Artificial Intelligence Techniques for Real-Time Predictive Maintenance in Industrial IoT Systems: A Comprehensive Analysis and Framework." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 271-313.
Prabhod, Kummaragunta Joel. "Leveraging Generative AI and Foundation Models for Personalized Healthcare: Predictive Analytics and Custom Treatment Plans Using Deep Learning Algorithms." Journal of AI in Healthcare and Medicine 4.1 (2024): 1-23.
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
Shahane, Vishal. "Harnessing Serverless Computing for Efficient and Scalable Big Data Analytics Workloads." Journal of Artificial Intelligence Research 1.1 (2021): 40-65.
Shanmugam, Lavanya, Ravish Tillu, and Manish Tomar. "Federated learning architecture: Design, implementation, and challenges in distributed AI systems." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)2.2 (2023): 371-384.
