3,755 research outputs found
Secure and linear cryptosystems using error-correcting codes
A public-key cryptosystem, digital signature and authentication procedures
based on a Gallager-type parity-check error-correcting code are presented. The
complexity of the encryption and the decryption processes scale linearly with
the size of the plaintext Alice sends to Bob. The public-key is pre-corrupted
by Bob, whereas a private-noise added by Alice to a given fraction of the
ciphertext of each encrypted plaintext serves to increase the secure channel
and is the cornerstone for digital signatures and authentication. Various
scenarios are discussed including the possible actions of the opponent Oscar as
an eavesdropper or as a disruptor
Secure exchange of information by synchronization of neural networks
A connection between the theory of neural networks and cryptography is
presented. A new phenomenon, namely synchronization of neural networks is
leading to a new method of exchange of secret messages. Numerical simulations
show that two artificial networks being trained by Hebbian learning rule on
their mutual outputs develop an antiparallel state of their synaptic weights.
The synchronized weights are used to construct an ephemeral key exchange
protocol for a secure transmission of secret data. It is shown that an opponent
who knows the protocol and all details of any transmission of the data has no
chance to decrypt the secret message, since tracking the weights is a hard
problem compared to synchronization. The complexity of the generation of the
secure channel is linear with the size of the network.Comment: 11 pages, 5 figure
Statistical mechanical aspects of joint source-channel coding
An MN-Gallager Code over Galois fields, , based on the Dynamical Block
Posterior probabilities (DBP) for messages with a given set of autocorrelations
is presented with the following main results: (a) for a binary symmetric
channel the threshold, , is extrapolated for infinite messages using the
scaling relation for the median convergence time, ;
(b) a degradation in the threshold is observed as the correlations are
enhanced; (c) for a given set of autocorrelations the performance is enhanced
as is increased; (d) the efficiency of the DBP joint source-channel coding
is slightly better than the standard gzip compression method; (e) for a given
entropy, the performance of the DBP algorithm is a function of the decay of the
correlation function over large distances.Comment: 6 page
Public channel cryptography by synchronization of neural networks and chaotic maps
Two different kinds of synchronization have been applied to cryptography:
Synchronization of chaotic maps by one common external signal and
synchronization of neural networks by mutual learning. By combining these two
mechanisms, where the external signal to the chaotic maps is synchronized by
the nets, we construct a hybrid network which allows a secure generation of
secret encryption keys over a public channel. The security with respect to
attacks, recently proposed by Shamir et al, is increased by chaotic
synchronization.Comment: 4 page
Multi-Choice Minority Game
The generalization of the problem of adaptive competition, known as the
minority game, to the case of possible choices for each player is
addressed, and applied to a system of interacting perceptrons with input and
output units of the type of -states Potts-spins. An optimal solution of this
minority game as well as the dynamic evolution of the adaptive strategies of
the players are solved analytically for a general and compared with
numerical simulations.Comment: 5 pages, 2 figures, reorganized and clarifie
Cryptography based on neural networks - analytical results
Mutual learning process between two parity feed-forward networks with
discrete and continuous weights is studied analytically, and we find that the
number of steps required to achieve full synchronization between the two
networks in the case of discrete weights is finite. The synchronization process
is shown to be non-self-averaging and the analytical solution is based on
random auxiliary variables. The learning time of an attacker that is trying to
imitate one of the networks is examined analytically and is found to be much
longer than the synchronization time. Analytical results are found to be in
agreement with simulations
Synchronization with mismatched synaptic delays: A unique role of elastic neuronal latency
We show that the unavoidable increase in neuronal response latency to ongoing
stimulation serves as a nonuniform gradual stretching of neuronal circuit delay
loops and emerges as an essential mechanism in the formation of various types
of neuronal timers. Synchronization emerges as a transient phenomenon without
predefined precise matched synaptic delays. These findings are described in an
experimental procedure where conditioned stimulations were enforced on a
circuit of neurons embedded within a large-scale network of cortical cells
in-vitro, and are corroborated by neuronal simulations. They evidence a new
cortical timescale based on tens of microseconds stretching of neuronal circuit
delay loops per spike, and with realistic delays of a few milliseconds,
synchronization emerges for a finite fraction of neuronal circuit delays.Comment: 12 pages, 4 figures, 13 pages of Supplementary materia
Genetic attack on neural cryptography
Different scaling properties for the complexity of bidirectional
synchronization and unidirectional learning are essential for the security of
neural cryptography. Incrementing the synaptic depth of the networks increases
the synchronization time only polynomially, but the success of the geometric
attack is reduced exponentially and it clearly fails in the limit of infinite
synaptic depth. This method is improved by adding a genetic algorithm, which
selects the fittest neural networks. The probability of a successful genetic
attack is calculated for different model parameters using numerical
simulations. The results show that scaling laws observed in the case of other
attacks hold for the improved algorithm, too. The number of networks needed for
an effective attack grows exponentially with increasing synaptic depth. In
addition, finite-size effects caused by Hebbian and anti-Hebbian learning are
analyzed. These learning rules converge to the random walk rule if the synaptic
depth is small compared to the square root of the system size.Comment: 8 pages, 12 figures; section 5 amended, typos correcte
Cluster Dynamics for Randomly Frustrated Systems with Finite Connectivity
In simulations of some infinite range spin glass systems with finite
connectivity, it is found that for any resonable computational time, the
saturatedenergy per spin that is achieved by a cluster algorithm is lowered in
comparison to that achieved by Metropolis dynamics.The gap between the average
energies obtained from these two dynamics is robust with respect to variations
of the annealing schedule. For some probability distribution of the
interactions the ground state energy is calculated analytically within the
replica symmetry assumptionand is found to be saturated by a cluster algorithm.Comment: Revtex, 4 pages with 3 figure
Finite size effects and error-free communication in Gaussian channels
The efficacy of a specially constructed Gallager-type error-correcting code
to communication in a Gaussian channel is being examined. The construction is
based on the introduction of complex matrices, used in both encoding and
decoding, which comprise sub-matrices of cascading connection values. The
finite size effects are estimated for comparing the results to the bounds set
by Shannon. The critical noise level achieved for certain code-rates and
infinitely large systems nearly saturates the bounds set by Shannon even when
the connectivity used is low
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