Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  look at iterated por generators $s_i = s_{i-1}^e {\rm mod} N$ for a random seed  s  0 *** ***  N  that in each iteration output a certain amount of bits.  show that heuristically an output of $(1-\frac 1 e)\log N$ most significant bits per iteration allows for efficient recovery of the whole sequence. This means in particular that the Blum-Blum-Shub generator should be used with an output of less than half of the bits per iteration and the RSA generator with  e  = 3 with less than a $\frac 1 3$-fraction of the bits.#R##N##R##N#Our method is lattice-based and introduces a new technique, which combines the benefits of two techniques, namely the method of linearization and the method of Coppersmith for finding small roots of polynomial equations.  call this new technique  unravelled linearization  .
['Mathias Herrmann', 'Alexander May']
I am writing a research paper on a idea Attacking Power Generators Using Unravelled Linearization: When Do We Output Too Much?
[{"from": "human", "value": "I am writing a research paper on a idea Attacking Power Generators Using Unravelled Linearization: When Do We Output Too Much?"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  look at iterated por generators $s_i = s_{i-1}^e {\\rm mod} N$ for a random seed  s  0 *** ***  N  that in each iteration output a certain amount of bits.  show that heuristically an output of $(1-\\frac 1 e)\\log N$ most significant bits per iteration allows for efficient recovery of the whole sequence. This means in particular that the Blum-Blum-Shub generator should be used with an output of less than half of the bits per iteration and the RSA generator with  e  = 3 with less than a $\\frac 1 3$-fraction of the bits.#R##N##R##N#Our method is lattice-based and introduces a new technique, which combines the benefits of two techniques, namely the method of linearization and the method of Coppersmith for finding small roots of polynomial equations.  call this new technique  unravelled linearization  ."}]