Hey, we have cryptanalytic methods that can infer any cipher, key, or plaintext unless you intelligently destroy as many patterns in the ciphertext as you can.
I don’t think this is true for ciphers that are anywhere near as complicated as the physical world is. For inferring models of limited complexity on limited numbers of variables in constrained form, however, there’re some pretty good algorithms in Pearl’s book Causality, based on conditional correlation and independence.
I don’t think this is true for ciphers that are anywhere near as complicated as the physical world is
Right—I tried to make clear that my concern is only about those phenomena that are less complex that the most complex broken cipher. For unknown-physical-law cases (i.e. we don’t even know the dynamics of the phenomenon), the comparison is to ciphers that can be broken even if you don’t know which cipher or public key is being used; for unknown-constants cases (where we know the form of the equations), that also includes ciphers requiring knowledge of the cipher and public key to break.
For inferring models of limited complexity on limited numbers of variables in constrained form, however, there’re some pretty good algorithms in Pearl’s book Causality, based on conditional correlation and independence.
True, but there are broken ciphers that are resistant to a cryptanalytic version of this, which suggests that Pearl isn’t covering all the ways to find regularity in data.
True, but there are broken ciphers that are resistant to a cryptanalytic version of this, which suggests that Pearl isn’t covering all the ways to find regularity in data.
Indeed. I haven’t finished Pearl yet, but from what I’ve read so far, it doesn’t look like his models can handle iteration, vector-typed variables, model priors other than one particular interpretation of Occam’s razor, or hidden variables more complicated than a two-variable correlation. So there’s a lot of theory left to build, and cryptanalysis may have some lessons for that theory.
I don’t think this is true for ciphers that are anywhere near as complicated as the physical world is. For inferring models of limited complexity on limited numbers of variables in constrained form, however, there’re some pretty good algorithms in Pearl’s book Causality, based on conditional correlation and independence.
Right—I tried to make clear that my concern is only about those phenomena that are less complex that the most complex broken cipher. For unknown-physical-law cases (i.e. we don’t even know the dynamics of the phenomenon), the comparison is to ciphers that can be broken even if you don’t know which cipher or public key is being used; for unknown-constants cases (where we know the form of the equations), that also includes ciphers requiring knowledge of the cipher and public key to break.
True, but there are broken ciphers that are resistant to a cryptanalytic version of this, which suggests that Pearl isn’t covering all the ways to find regularity in data.
Indeed. I haven’t finished Pearl yet, but from what I’ve read so far, it doesn’t look like his models can handle iteration, vector-typed variables, model priors other than one particular interpretation of Occam’s razor, or hidden variables more complicated than a two-variable correlation. So there’s a lot of theory left to build, and cryptanalysis may have some lessons for that theory.