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Concept

Modular exponentiation

Related lectures (30)

Covers Barrett reduction, Montgomery form of integers, and efficient Montgomery product computation.

Explores windowing exponentiation with base-B representation and precomputed small powers of x for efficient computation.

Covers exponentiation, RSA encryption, big integer multiplication, and primitive roots.

Introduces symmetric-key cryptosystems and the key concepts for secure communication.

Explores cyclic groups, generators, isomorphisms, and the discrete logarithm problem in depth, highlighting their importance and applications.

Explores quantum random number generation, discussing the challenges and implementations of generating good randomness using quantum devices.

Covers Shor's factoring algorithm, which efficiently factors large numbers using quantum computers.

Covers binary addition, multiplication, and modular exponentiation of integers.

Covers examples of modular exponentiation, complexities, Lame's Theorem, Collatz Conjecture, and prime numbers.

Covers regular languages, encoding, alphabets, words, languages, concatenation, exponentiation, and iterative closure.

Showcases examples of complex numbers in polar form and their operations.

Covers binary addition, multiplication, and modular exponentiation in cryptography.

Covers binary addition, prime numbers, and the sieve of Eratosthenes in number theory.

Explores public key cryptography, focusing on RSA, key generation, encryption, decryption, security challenges, and vulnerabilities.

Covers the fast exponentiation algorithm and its time complexity, prime number properties, and the El-Gamal encryption scheme.

Covers the principles of classes, encapsulation, and modular programming in C++.

Covers commutative groups and their significance in cryptography.

Explores discrete exponentiation complexity, cyclic groups, and practical cryptography, including popular algorithms like Diffie-Hellman and RSA.

Explores finding the equation of the tangent line to a function's graph at a point.

Covers mathematical induction, recursively defined functions, and iterative algorithms for problem-solving.