4,526 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
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
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
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
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
Coloring random graphs
We study the graph coloring problem over random graphs of finite average
connectivity . Given a number of available colors, we find that graphs
with low connectivity admit almost always a proper coloring whereas graphs with
high connectivity are uncolorable. Depending on , we find the precise value
of the critical average connectivity . Moreover, we show that below
there exist a clustering phase in which ground states
spontaneously divide into an exponential number of clusters and where the
proliferation of metastable states is responsible for the onset of complexity
in local search algorithms.Comment: 4 pages, 1 figure, version to app. in PR
Dynamics of neural cryptography
Synchronization of neural networks has been used for novel public channel
protocols in cryptography. In the case of tree parity machines the dynamics of
both bidirectional synchronization and unidirectional learning is driven by
attractive and repulsive stochastic forces. Thus it can be described well by a
random walk model for the overlap between participating neural networks. For
that purpose transition probabilities and scaling laws for the step sizes are
derived analytically. Both these calculations as well as numerical simulations
show that bidirectional interaction leads to full synchronization on average.
In contrast, successful learning is only possible by means of fluctuations.
Consequently, synchronization is much faster than learning, which is essential
for the security of the neural key-exchange protocol. However, this qualitative
difference between bidirectional and unidirectional interaction vanishes if
tree parity machines with more than three hidden units are used, so that those
neural networks are not suitable for neural cryptography. In addition, the
effective number of keys which can be generated by the neural key-exchange
protocol is calculated using the entropy of the weight distribution. As this
quantity increases exponentially with the system size, brute-force attacks on
neural cryptography can easily be made unfeasible.Comment: 9 pages, 15 figures; typos correcte
Mean Field Behavior of Cluster Dynamics
The dynamic behavior of cluster algorithms is analyzed in the classical mean
field limit. Rigorous analytical results below establish that the dynamic
exponent has the value for the Swendsen-Wang algorithm and
for the Wolff algorithm.
An efficient Monte Carlo implementation is introduced, adapted for using
these algorithms for fully connected graphs. Extensive simulations both above
and below demonstrate scaling and evaluate the finite-size scaling
function by means of a rather impressive collapse of the data.Comment: Revtex, 9 pages with 7 figure
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