arXiv subjects

Arimondo Scrivano

Publications and source records attributed to Arimondo Scrivano.

2 recordsLinked to original sources

arXiv2025

A Comparative Study of Classical and Post-Quantum Cryptographic Algorithms in the Era of Quantum Computing

The advent of quantum computing poses a significant threat to the foundational cryptographic algorithms that secure modern digital communications. Protocols such as HTTPS, digital certificates, and public key infrastructures (PKIs) heavily rely on cryptographic primitives like RSA, ECC, and Diffie-Hellman, which are vulnerable to quantum attacks -- most notably Shor's algorithm. This paper presents a comprehensive comparative analysis between classical cryptographic algorithms currently in widespread use and emerging post-quantum cryptographic schemes designed to withstand quantum adversaries. We review the cryptographic mechanisms underpinning modern internet security, outline the mathematical foundations of quantum attacks, and evaluate the security, performance, and implementation feasibility of quantum-resistant alternatives such as Kyber, Dilithium, and Falcon. Additionally, we assess the hybrid approaches currently being explored by institutions and tech companies to enable a smooth transition to post-quantum cryptography. By providing an in-depth comparison, this study aims to guide researchers, developers, and policymakers in understanding the critical implications of quantum computing on cryptographic infrastructures and the necessary steps for securing communications in the quantum era.

cs.ET↗

arXiv2025

A Comparative Study of Recommender Systems under Big Data Constraints

Recommender Systems (RS) have become essential tools in a wide range of digital services, from e-commerce and streaming platforms to news and social media. As the volume of user-item interactions grows exponentially, especially in Big Data environments, selecting the most appropriate RS model becomes a critical task. This paper presents a comparative study of several state-of-the-art recommender algorithms, including EASE-R, SLIM, SLIM with ElasticNet regularization, Matrix Factorization (FunkSVD and ALS), P3Alpha, and RP3Beta. We evaluate these models according to key criteria such as scalability, computational complexity, predictive accuracy, and interpretability. The analysis considers both their theoretical underpinnings and practical applicability in large-scale scenarios. Our results highlight that while models like SLIM and SLIM-ElasticNet offer high accuracy and interpretability, they suffer from high computational costs, making them less suitable for real-time applications. In contrast, algorithms such as EASE-R and RP3Beta achieve a favorable balance between performance and scalability, proving more effective in large-scale environments. This study aims to provide guidelines for selecting the most appropriate recommender approach based on specific Big Data constraints and system requirements.

cs.IR↗