Sebastia Agramunt-Puig’s article on Medium, “Number Theory for Cryptography and Privacy Preserving Machine Learning,” offers a comprehensive look at the foundational mathematical principles underpinning modern cryptography and its application in privacy-preserving machine learning. The article begins with a primer on divisibility and the greatest common divisor, laying the groundwork for understanding more complex concepts in number theory.
Agramunt-Puig then delves into modular arithmetic, a cornerstone in cryptographic algorithms, explaining its relevance and application in cryptography. He elucidates on how modular arithmetic leads to the formation of mathematical structures like groups, rings, and fields, which are pivotal in the construction of cryptographic systems.
The author further explores the intricacies of rings and fields, highlighting their significance in cryptography, especially in the context of elliptic curves and key exchange protocols. He ties these mathematical concepts directly to practical applications in cryptography and privacy-preserving machine learning, making a complex subject approachable for readers with varying levels of familiarity with the topic.
In conclusion, Agramunt-Puig’s article is a deep dive into the mathematical underpinnings of cryptography, offering readers a clear understanding of how number theory is applied in modern cryptographic techniques and privacy-preserving technologies. His ability to bridge theoretical concepts with practical applications makes this article a valuable resource for anyone interested in cryptography, data privacy, and machine learning.
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For a detailed read, the full article is available here.