Extremal optimization (EO) [1] is a metaheuristic method [2]
which is quite similar to the way the immune system (IS) renews its
cells. This dynamic is called extremal dynamics [3]. It can explain
the long-range memory of the immune system even without the
persistence of antigens. The reason is that if a system evolves
according to such dynamics then the annihilation probability
for a clone (a type of cells) that has already survived for time t is
proportional to 1/ (1+ tc) , where c is a positive constant. Therefore,
the longer it lives the higher the probability that it will continue to
survive. This is the memory effect observed in the immune system.
Consider a system of N elements, each element assigned a single
scalar variable x(i), i = 1,2…, N drawn from the fixed probability
distribution function p(x). For every time step, the element with the
smallest value in the system is selected and renewed by assigning a
new value which is drawn from p(x). It is assumed that no two x(i)
can take the same value.
Definition 1
For the above system the typical values of x(i) increase
monotonically in time. This means that any renewed element is
likely to have a smaller x(i) than the bulk, and hence a shorter than
average lifespan until it is again renewed. Corresponding, elements
that have not been renewed for some time will have a longer than
average life expectancy. This separation between the shortest and
the longest-lived elements will become more pronounced as the
system evolves and the average x(i) in the bulk increases.
This phenomenon is called long-time memory.
Proposition 2
Extremally driven systems can generally be expected to exhibit
long-term memory [1].
Proposed algorithm:
i. Apply public key cryptography [4,5] using binary notation
to build the initial state x(i) i=1, 2,,n common to both sender A
and receiver B.
ii. A chooses the matrix J(i,k) and runs the spin glass
model [1] using the extremal optimization algorithm and the
Hamiltonian H = ΣJ (i, k )× (i)× (k ) Such that the final state
xf(i) represents the message.
iii. A sends J(i,k) to B to derive the message.
An advantage of this algorithm is that it is applicable even for
small computers.
I like to comment on the present situation of post-quantum
cryptography. Quantum computers have already been made
by Google and IBM. They are capable of breaking the standard
cryptography e.g. RSA and elliptic curve cryptography [4]. Therefore,
studying post-quantum cryptography is essential. An excellent
candidate is quantum cryptography, but it is local [6]. Presently one
of the strongest candidates in the National Institute of Standards
and Technology (NIST) competition is lattice based cryptography
[7]. Google chrome already uses lattice-based cryptography. But lattice-based cryptography is known to have a gap between theory
and practice. More mathematical studies are needed for it [7].
BoettcherS, PercusA (2001) Optimization with Extremal DynamicsPhysical Review Letters86(23): 5211-5214.
Jeffrey, Hoffstein, Jill, Pipher, Joseph H, etal. (2004) An Introduction to Mathematical Cryptography. Springer.
KnospeHeiko (2019)A course in cryptography. Pure and Applied Undergraduate. ProvidenceRI AmericanMathematical Society 40: 323.
Mitch Leslie (2019) Quantum Cryptography via Satellite Engineering 5(3): 353-354.
Lidong Chen, Dustin Moody (2020)New Mission and Opportunity for Mathematics Researchers. Cryptography inthe Quantum Era Advances in Mathematics of Communications14(1):161.