# 4.1: What Qualifies as a "Computationally Infeasible" Attack?

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Nash’s letters also spell out the importance of distinguishing between computations that take a polynomial amount of time and those that take an exponential amount of time.^{3}Throughout computer science, polynomial-time is used as a formal definition of “efficient,” and exponential-time (and above) is synonymous with “intractible.”

In cryptography, it makes a lot of sense to not worry about guaranteeing security in the presence of attackers with unlimited computational resources. Not even the most powerful nation-states can invest 2^{100} CPU cycles towards a cryptographic attack. So the modern approach to cryptography (more or less) follows Nash’s suggestion, demanding that breaking a scheme requires exponential time.

Definition \(\PageIndex{1}\)

*A program runs in polynomial time if there exists a constant c* > 0

*such that for all sufficiently long input strings x, the program stops after no more than O*(|

*x*|

^{c}) steps.

Polynomial time is not a perfect match to what we mean by “efficient.” Polynomial time includes algorithms with running time Θ(*n*^{1000}), while excluding those with running time Θ(*n*^{log log log n}). Despite that, it’s extremely useful because of the following *closure property*: repeating a polynomial-time process a polynomial number of times results in a polynomial-time process overall.

**Security Parameter**

The definition of polynomial-time is *asymptotic*, since it considers the behavior of a computer program *as the size of the inputs grows to infinity*. But cryptographic algorithms often take multiple different inputs to serve various purposes. To be absolutely clear about our “measuring stick” for polynomial time, we measure the efficiency of cryptographic algorithms (and adversaries!) against something called the **security parameter**, which is the number of bits needed to represent secret keys and/or randomly chosen values used in the scheme. We will typically use *λ* to refer to the security parameter of a scheme.

It’s helpful to think of the security parameter as a tuning knob for the cryptographic system. When we dial up this knob, we increase the size of the keys in the system, the size of all associated things like ciphertexts, and the required computation time of the associated algorithms. Most importantly, the amount of effort required by the honest users grows reasonably (as a polynomial function of the security parameter) while the effort required to violate security increases faster than any (polynomial-time) adversary can keep up.

**Potential Pitfall: Numerical Algorithms**

The public-key cryptographic algorithms that we will see are based on problems from abstract algebra and number theory. These schemes require users to perform operations on very large numbers. We must remember that representing the number *N* on a computer requires only ┌log_{2} (*N*+1)┐ bits. This means that ┌log_{2} (*N*+1)┐, rather than *N*, is our security parameter! We will therefore be interested in whether certain operations on the number *N* run in polynomial-time as a function of ┌log_{2} (*N* + 1)┐, rather than in *N*. Keep in mind that the difference between running time *O*(log *N*) and *O*(*N*) is the difference between writing down a number and counting to the number.

For reference, here are some numerical operations that we will be using later in the class, and their known efficiencies:

Efficient algorithm known: |
No known efficient algorithm |

Computing GCDs | Factoring integers |

Arithmetic mod n |
Computing \(\phi(n)\) given n |

Inverses mod n |
Discrete logarithm |

Exponentiation mod n |
Square roots mod composite n |

By “efficient,” we mean polynomial-time. However, all of the problems in the right-hand column *do* have known polynomial-time algorithms on quantum computers.

^{3}Nash was surprisingly ahead of his time here. Polynomial-time wasn’t formally proposed as a natural definition for “efficiency” in computation until Alan Cobham, 10 years after Nash’s letter was written (Alan Cobham, The intrinsic computational difficulty of functions, in Proc. Logic, Methodology, and Philosophy of Science II, 1965). Until Nash’s letters were declassified, the earliest well-known argument hinting at the importance of polynomial-time was in a letter from Kurt Gödel to John von Neumann. But that letter is not nearly as explicit as Nash’s, and was anyway written a year later.