MHB How Does Integer Factorization Impact Modern Cryptography and Beyond?

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Integer factorization is crucial for modern cryptography, particularly because it underpins the security of widely used public key systems like RSA. An efficient integer factorization algorithm could compromise these systems, allowing for the rapid computation of the order of multiplicative groups and extending polynomial time algorithms from primes to composites. Beyond cryptography, such an algorithm could also enhance understanding of related problems, including the discrete logarithm problem and prime distribution. While no classical algorithm is known to efficiently factor integers, quantum computing offers potential solutions through Shor's algorithm. The reliance on the difficulty of factorization remains a cornerstone of cryptographic security today.
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Why is factorization of integers important? What are the real life applications of factorization? Are there are examples which have a real impact.
 
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If you had an efficient integer factorization algorithm you could do the following:
- efficiently extend all polynomial time algorithms modulo primes to composites via the CRT
- efficiently compute the order of the multiplicative group of integers modulo a composite (thereby breaking the most used public key encryption scheme in the world, namely RSA, and making millions of dollars)

Furthermore there is a good chance such an algorithm could be modified or extended to apply to other important and related classes of problems, such as the discrete logarithm problem, or be used to gain insight into e.g. the distribution of primes. It is unknown if such an algorithm even exists for classical computers, but integer factorization is known to be (theoretically) polynomial time on a sufficiently large quantum computer (Shor's algorithm).

Basically, it is important, because a lot of today's public key cryptography relies on the assumption that factorization is actually hard, an assumption which has been holding very well so far. A fast way to factorize would also have far-reaching consequences beyond this in more theoretical fields, as a lot of problems involving modular arithmetic basically need to factorize their input to work properly, which can be expensive in some cases if the modulus is of unknown provenance (though in many cases it doesn't matter as the modulus has a special form or its factorization is known in advance).
 
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Thread 'Erroneously  finding discrepancy in transpose rule'
Obviously, there is something elementary I am missing here. To form the transpose of a matrix, one exchanges rows and columns, so the transpose of a scalar, considered as (or isomorphic to) a one-entry matrix, should stay the same, including if the scalar is a complex number. On the other hand, in the isomorphism between the complex plane and the real plane, a complex number a+bi corresponds to a matrix in the real plane; taking the transpose we get which then corresponds to a-bi...

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