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CSE442T Introduction to Cryptography (Lecture 5)

Chapter 2: Computational Hardness

Proving that there are one-way functions relies on assumptions.

Factoring Assumption: \forall \mathcal{A}, \exist \epsilon (n), let p,q\in \Pi_n,p,q<2^n


P[p\gets \Pi_n;q\gets \Pi_n;N=p\cdot q:\mathcal{A}(N)\in \{p,q\}]<\epsilon(n)

Evidence: To this point, best known procedure to always factor has run time O(2^{\sqrt{n}\sqrt{log(n)}})

Distribution of prime numbers:

  • We have infinitely many prime
  • Prime Number Theorem \pi(n)\approx\frac{n}{\ln(n)}, that means, \frac{1}{\ln n} of all integers are prime.

We want to (guaranteed to) find prime:

\pi(n)>\frac{2^n}{2n}

e.g.


P[x\gets \{0,1\}^n:x\in prime]\geq {\frac{2^n}{2n}\over 2^n}=\frac{1}{2n}

Theorem:


f_{mult}:\{0,1\}^{2n}\to \{0,1\}^{2n},f_{mult}(x_1,x_2)=x_1\cdot x_2

Idea: There are enough pairs of primes to make this difficult.

Reminder: Weak on-way if easy to compute and \exist p(n), P[\mathcal{A}\ \text{inverts=success}]<1-\frac{1}{p(n)} P[\mathcal{A}\ \text{inverts=failure}]>\frac{1}{p(n)} high enough

Prove one-way function (under assumptions)

To prove f is on-way (under assumption)

  1. Show \exists p.p.t solves f(x),\forall x.
  2. Proof by contradiction.
    • For weak: Provide p(n) that we know works.
      • Assume \exists \mathcal{A} such that P[\mathcal{A}\ \text{inverts}]>1-\frac{1}{p(n)}
    • For strong: Provide p(n) that we know works.
      • Assume \exists \mathcal{A} such that P[\mathcal{A}\ \text{inverts}]>\frac{1}{p(n)}

Construct p.p.t \mathcal{B} which uses \mathcal{A} to solve a problem, which contradicts assumption or known fact.

Back to Theorem:

We will show that p(n)=8n^2 works.

We claim \forall \mathcal{A},


P[(x_1,x_2)\gets \{0,1\}^{2n};y=f_{mult}(x_1,x_2):f(\mathcal{A}(y))=y]<1-\frac{1}{8n^2}

For the sake of contradiction, suppose


\exists \mathcal{A} \textup{ such that} P[\mathcal{A}\ \text{inverts}]>1-\frac{1}{8n^2}

We will use this \mathcal{A} to design p.p.t B which can factor 2 random primes with non-negligible prob.

def A(y):
    # the adversary algorithm
    # expecting N to be product of random integer, don't need to be prime

def is_prime(x):
    # test if x is a prime

def gen(n):
    # generate number up to n bits

def B(y):
    # N is the input cipher
    x1,x2=gen(n),gen(n)
    p=x1*x2
    if is_prime(x1) and is_prime(x2):
        return A(p)
    return A(y)

How often does \mathcal{B} succeed/fail?

\mathcal{B} fails to factor N=p\dot q, if:

  • x and y are not both prime
    • P_e=1-P(x\in \Pi_n)P(y\in \Pi_n)\leq 1-(\frac{1}{2n})^2=1-\frac{1}{4n^2}
  • if \mathcal{A} fails to factor
    • P_f<\frac{1}{8n^2}

So


P[\mathcal{B} \text{ fails}]\leq P[E\cup F]\leq P[E]+P[F]\leq (1-\frac{1}{4n^2}+\frac{1}{8n^2})=1-\frac{1}{8n^2}

So


P[\mathcal{B} \text{ succeed}]\geq \frac{1}{8n^2} (\text{non-negligible})

This contradicting factoring assumption. Therefore, our assumption that \mathcal{A} exists was wrong.

Therefore \forall \mathcal{A}, P[(x_1,x_2)\gets \{0,1\}^{2n};y=f_{mult}(x_1,x_2):f(\mathcal{A}(y))=y]<1-\frac{1}{8n^2} is wrong.