Imagine modeling the input(plaintext) - output(ciphertext) pairs of an encryption algorithm as a data science problem. Very informally, the strength of an encryption scheme is measured by the randomness (unpredictability, or entropy) of the output. This is counter-intuitive to classic regression problems, which are frequently used for prediction. Say, informally, the strength of an encryption scheme is determined by the number of such input-output pairs needed beyond which it becomes predictable. How do we model this as data science problem? Given all pairs of two different encryption schemes, can we determine which is stronger just by using the input-output pairs of both the schemes? Is there any other way apart from regression to solve this problem?
How to model this un predicatability problem?
machine learning;regression