Statistical distribution, host for encrypted information
ABSTRACT The statistical distribution, when determined from an incomplete set of constraints, is shown to be suitable as host for encrypted information. We design an encoding/decoding scheme to embed such a distribution with hidden information. The encryption security is based on the extreme instability of the encoding procedure. The essential feature of the proposed system lies in the fact that the key for retrieving the code is generated by random perturbations of {\em {very small value}}. The security of the proposed encryption relies on the security to interchange the secret key. Hence, it appears as a good complement to the quantum key distribution protocol.
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ABSTRACT: The recently introduced approach for Encrypted Image Folding is generalized to make it self-contained. The goal is achieved by enlarging the folded image so as to embed all the necessary information for the image recovery. The need for extra size is somewhat compensated by considering a transformation with higher folding capacity. Numerical examples show that the size of the resulting cipher image may be significantly smaller than the plain text one. The implementation of the approach is further extended to deal also with color images.Physica A: Statistical Mechanics and its Applications 12/2012; 391(23):5858–5870. · 1.72 Impact Factor - SourceAvailable from: arxiv.org[Show abstract] [Hide abstract]
ABSTRACT: A novel strategy to encrypt covert information (code) via unitary projections into the null spaces of ill-conditioned eigenstructures of multiple host statistical distributions, inferred from incomplete constraints, is presented. The host pdf's are inferred using the maximum entropy principle. The projection of the covert information is dependent upon the pdf's of the host statistical distributions. The security of the encryption/decryption strategy is based on the extreme instability of the encoding process. A self-consistent procedure to derive keys for both symmetric and asymmetric cryptography is presented. The advantages of using a multiple pdf model to achieve encryption of covert information are briefly highlighted. Numerical simulations exemplify the efficacy of the model. Comment: 18 pages, 4 figures. Three sentences expanded to emphasize detail. Typos correctedPhysics Letters A 07/2006; · 1.63 Impact Factor
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arXiv:cond-mat/0504788v1 [cond-mat.stat-mech] 29 Apr 2005
Statistical distribution, host for encrypted
information
L. Rebollo-Neira
Aston University
Birmingham B4 7ET, United Kingdom A. Plastino
Instituto de F´ ısica La Plata (IFLP)
Universidad Nacional de La Plata and CONICET∗
C.C. 727, 1900 La Plata, Argentina
Abstract
The statistical distribution, when determined from an incomplete set of
constraints, is shown to be suitable as host for encrypted information. We
design an encoding/decoding scheme to embed such a distribution with hidden
information. The encryption security is based on the extreme instability of the
encoding procedure. The essential feature of the proposed system lies in the
fact that the key for retrieving the code is generated by random perturbations
of very small value. The security of the proposed encryption relies on the
security to interchange the secret key. Hence, it appears as a good complement
to the quantum key distribution protocol.
PACS: 05.20.-y, 02.50.Tt, 02.30.Zz, 07.05.Kf
1 Introduction
Cryptography is the art of code making and cryptology the art of secure communica-
tions. Recently, quantum mechanics has made a remarkable entry in the field [1–3].
The most straightforward application of quantum cryptology is the distribution of
secret keys. This problem is refereed in the cryptography literature to as the key
distribution problem. Classical methods for securing a secret key are based on the
assumed difficulty of computing certain functions [4,5]. Quantum encryption pro-
vides a way of agreeing on a secret key without making this assumption. The first
∗Argentina’s National Research Council
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quantum key distribution protocol was proposed in 1984 by Bennett and Brassard [6]
and there are already rigorous proofs of its security [7–9]. The list of recent con-
tributions concerning the security and implementation of quantum key distribution
protocols is certainly extensive. Just as a sample one should mention [10–14].
The amount of information that can be transmitted by a quantum transmis-
sion is not very large, but by means of secret-key cryptographic algorithms a large
amount of information can be secured. In this paper we set the foundations for a
statistical distribution based encryption procedures, which will be shown to be good
complements to quantum cryptology. It will be here demonstrated that the statis-
tical distribution of a physical system is a suitable host for encrypted information
and we will discuss a method for embedding encrypted messages without affecting
the physical content of the distribution.
The fact that a physical system can be assumed to be well described by a particu-
lar parametric class of statistical distribution entails, in most situations, the assump-
tion of a great deal of prior information. Indeed, the functional form of most cele-
brated statistical distribution (Gibbs-Boltzmann, Fermi-Dirac, Bose-Einstein distri-
butions, etc) can be derived from a few constraints expressed as mean values of some
observable, and the optimisation of a convex function called entropy or information
measure [15]. Thus, if a so obtained distribution happens to be the right one to
describe a particular system, one can think of the optimisation process as replacing
the information required to determine a unique distribution for the system.
We would like to think that, when one “guesses” (or derives) a distribution
from incomplete information, one also generates an “invisible reservoir” to place
information. This is not an original remark, of course, but an elemental result of
linear algebra: associated to a rank deficient transformation there are two spaces,
the range and null spaces of the transformation. The latter is an “invisible” space
in the sense that all its elements are mapped to zero by the transformation.
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In this Communication we show that the invisible reservoir is an appropriate
host for storing covert information. We present an encoding/decoding scheme that
allows to store a great amount of hidden information as storing the distribution of a
physical system. The main idea is to make use of the null space of the transformation
generated by the constraints that should be fulfilled by the distribution. The security
of the system is guaranteed by designing a (in the popular meaning of the vocable,
not in the technical one) “chaotic” encoding procedure. This is achieved by means
of random perturbations to an extremely sensitive encoding process. The random
perturbations of very small values provide one thereby with the key for recovering the
code. This is the most remarkable feature of the our proposal: the key for retrieving
the hidden information is just a tiny number that accounts for the perturbation
that has been used for encoding purposes. Hence, the relevance of this proposal in
relation to quantum cryptology, and vice versa, since the security of our proposal
depends on a secure key interchange. It is appropriate to remark that the most
notable difference between this approach and chaotic cryptosystems [16,17] is that
the theory underlying our approach is essentially a linear one.
The idea of making use of an “unstable system” for encryption has been suc-
cessfully applied to over-sampling of Fourier coefficients for transmitting hidden
messages as transmitting a signal [18]. Here we use equivalent ideas. We assume
that, in addition to some constraints, we have the information on the process by
which the statistical distribution is univocally determined. This process is usually
the optimisation of a convex function (entropy). The particular expression for the
entropy may be a matter of controversy, though. For our purposes the choice of
the entropic measure is not relevant at all. What is important here is the convex-
ity property to ensure a unique solution. The selection of the appropriate entropic
measure is crucial, of course, to determine the right distribution for the physical
system. Nevertheless, this has no relation whatsoever with our encryption scheme.
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The paper is organised as follows: In section II we introduce the notation together
with an encoding/decoding scheme for embedding a statistical distribution with
hidden information. The procedure is illustrated by a numerical simulation in section
III and some conclusions are drawn in section IV.
2 Embedding the statistic distribution
We restrict considerations to finite dimensional classical statistic systems, or equiv-
alently, to a quantum system represented by a distribution constructed from com-
mutative operators. In both cases the mean value of, say M, physical observa-
tions xo
1,xo
2,...,xo
i,...xo
M, each of which is the expectation value of a random vari-
able that takes values xi,n ; n = 1,...,N according to a probability distribution
pn; n = 1,...,N is expressed as:
xo
i
=
N
?
n=1
pnxi,n
;i = 1,...,M
1 =
N
?
n=1
pn
(1)
Usually the number M of available measurements is much less than the dimension
N of the probability space. In order to assert a unique distribution for the system
at hand one has to adopt a decision criterion, which is frequently implemented
through the maximisation of a convex measure on the probability distribution. Such
a measure, called entropy or information measure, takes different forms. Here we
simple assume that the distribution characterising a given physical system is agreed
to be determined by a fixed set of constraints of the form (1) and the optimisation
of a convex function that we denote S.
For the sake of a handy notation we use Dirac notation to represent vectors.
Thus, the probability distribution is represented as the ket |p? ∈ RNwhich, by
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denoting as |n?, n = 1,...,N the standard basis in RN, can be expressed as
|p? =
N
?
n=1
|n??n|p? =
N
?
n=1
pn|n?. (2)
We also define a vector |xo? ∈ RMof components xo
1,xo
2,...,xo
M,1 and an operator
ˆA : RN→ RM+1given by
ˆA =
N
?
n=1
|xn??n|.(3)
Vectors |xn? ∈ RM+1, n = 1,...,N are defined in such a way that ?i|xn? = xi,n,i =
1,...,M + 1 with xM+1,n= 1. Hence,
|xn? =
M+1
?
i=1
|i??i|xn? =
M+1
?
i=1
xi,n|i?. (4)
We are now in a position to joint the constraints (1) together in the equation
|xo? =ˆA|p?. (5)
SinceˆA is a rank deficient operator, we know from elemental linear algebra that the
general solution to the under-determined system (5) can be expressed in the form:
|p? =ˆA′−1|xo? + |p′?,(6)
whereˆA′−1is the pseudo inverse ofˆA (i.e. the inverse of the restriction ofˆA to
range(ˆA)) and |p′? a vector in the null space of that operator. Consequently, all the
information the probability distribution contains concerning the data is expressible
in the fashionˆA′−1|xo?. On the contrary, the component |p′? is completely indepen-
dent of the data, but strongly dependent on the selection criterion that is adopted
to decide on one particular solution among the infinitely many solutions the system
(6) has.
The fact that all distributions of the form |˜ p? =ˆA′−1|xo? + |p′? with |p′? ∈
null(ˆA) are capable of reproducing the constraints vector |xo? provides us with a
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framework for the purpose of storing encrypted information while storing the statis-
tical distribution of a physical system. At the encoding step we make the following
assumptions:
i) The number M + 1 of the independent linear equations which are used to
determine the statistical distribution of a given system is fixed. The expected
values generating the equations are assumed to be known.
ii) The probability distribution characterising the system arises by optimisation
of a convex function S, subjected to the M + 1 linear constraints described
above.
Assumptions i) and ii) entail the availability, at this stage, of vector |p? and
operatorˆA. The vectors spanning the range and null spaces of this operator can be
determined by computing the the eigenvectors of operatorˆG =ˆA†ˆA. Let us denote
as |ηn?, n = 1,...,N − (M + 1) the normalised eigenvectors corresponding to zero
eigenvalues. We use these vectors to define operatorˆU : RN?→ RN−(M+1)as
ˆU =
N−M−1
?
n=1
|n??ηn| (7)
This operator is termed decoding operator, and its adjoint,ˆU†, encoding operator.
UsingˆU†a basic code of N − (M + 1) numbers is constructed as follows: Let the
N − (M + 1)numbers be the N − (M + 1)-components ?n|q? = qn,n = 1,...,N −
(M + 1) of vector |q? ∈ RN−(M+1)and define:
|pc? =ˆU†|q? =
N−M−1
?
n=1
|ηn??n|q?. (8)
Given a distribution |p?, amenable to be determined from the optimisation of an
entropy measure S and a set of M + 1 constraints, the code |q? is embedded in the
distribution through the process below.
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Encoding process
• Compute vector |pc? as in (8).
• Add |p? and |pc? to construct
|˜ p? = |p? + |pc?.
Decoding process
• Use the vector |˜ p? to recover the data |xo? as
|xo? =ˆA|˜ p?
• Use the data |xo? to determine, by optimisation of S, the distribution |p?.
From |˜ p? and |p? compute vector
|pc? = |˜ p? − |p?.
• Use the decoding operatorˆU to obtain the encrypted code by noticing that,
sinceˆUˆU†is the identity operator in RN−(M+1), from (8) one has:
|q? =ˆU|pc?. (9)
Note that the success of the above encoding/decoding scheme relies on the possibility
of ordering the eigenvectors in the null space. This is perfectly possible by fixing
the numerical method for computing the eigenvectors ofˆG. However, the process is
extremely unstable, as a tiny perturbation to any of the matrix elements of operator
ˆG produces a huge effect in the eigenvectors of zero eigenvalues. This “chaotic” (in
the popular sense) behaviour of the eigenvectors in the null space provides, naturally,
the security key for retrieving the encrypted code. Indeed, consider that ǫ is a very
small number (order of 10−13, say) that we add to one of the matrix elements ofˆG.
Such a tiny perturbation does not yield any detectable effect in the reconstruction
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of the distribution |p? but an enormous effect with regard to the eigenvectors in the
null space. Hence, as illustrated by the examples of the next section, the perturbation
ǫ provides the safety key of our encoding/decoding scheme.
2.1 Numerical Examples
Consider that a probability distribution concerning an event space of dimension
N = 401 is appropriately determined, by the Jaynes maximum entropy formalism,
from the normalisation to unity constraint and the first four moments of a random
variable xn,n = 1,...,401 that takes values ranging from x1= −1 to x401= 1 with
uniform increment ∆ = 1/400. Thus the distribution, which arises by maximisation
of the Shannon’s entropy S = −?401
n=1pnlnpnsubjected to the given constraints,
has the form:
pn = e−λo−λ1xn−λ2x2
401
?
n=1
n−λ3x3
n−λ4x4
n
eλo
=e−λ1xn−λ2x2
n−λ3xn−λ4x4
n. (10)
The parameters λ1,λ2,λ3and λ4are determined from the equations
xo
i=
?401
?401
n=1xi
n=1e−λ1xn−λ2x2
ne−λ1xn−λ2x2
n−λ3x3
n−λ4x4
n
n−λ3x3
n−λ4x4
n
,i = 1,...,4(11)
For xo
1= −0.0224,xo
2= 0.1048,xo
3= −0.0124,xo
4= 0.0284 one obtains λ1 =
−0.3,λ2= 3,λ3= 2,λ4= 3.8. The operatorˆA has a 5 × 401 matrix representation
of elements xi,n= xi
n,i = 1,...,4; n = 1,...,401 and x5,n= 1,n = 1,...,401. We
construct operatorˆG =ˆA†ˆA and compute its eigenvectors. The 396 eigenvectors
corresponding to zero eigenvalues are used to construct the encoding operatorˆU†
to encrypt a code |q? of 396 numbers. These numbers, each of which consists of 15
digits, are taken randomly from the [0, 1] interval. We now proceed as indicated in
the encoding process of the previous section: We construct the vector |pc? =ˆU†|q?
and add it to the distribution |p? to obtain vector |˜ p? = |p? + |pc?. This vector con-
tains both, the information on the physical system and the code. In order to retrieve
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such information we use |˜ p? to generate the constraints as xo
i= ?i|ˆA|˜ p?, i = 1,...,5.
Since, by construction,ˆA|pc? = 0, the constraints are generated from |˜ p? with high
precision. We use then this values to solve for the parameters of the distribution so
us to recover |p?. Vector |p? allows to obtain vector |pc? from the available vector
|˜ p? as |pc? = |˜ p? − |p?. The code is thus retrieved by the operation |q? =ˆU†|pc?.
Table 1 gives five of the 396 code numbers. The second column corresponds to the
reconstructed numbers. As can be observed the quality of the reconstruction is ex-
cellent. In order to give a measure assessing the reconstruction of all numbers, let
us denote by |qr? the reconstructed code and define the error of the reconstruction
as δr= |||q? − |qr?||. The value of δris in this case 4.8 × 10−14.
Let us now distort the matrix representation of operatorˆG by adding a number
ǫ = 2.9 × 10−13to one of its elements, say the element at the first row and fifth
column. If we repeat the process using the distorted matrix the outcomes are the
following: The perturbation has no detectable effect in the reconstruction of the
distribution |p?. However, if we intend to reconstruct the code without considering
the perturbation, what we obtain has no relation whatsoever with the true code
(see the 3rd column of Table 1). The error of the reconstruction is δr= 17.25.
Since the recovery of the code is only possible if the value of the perturbation is
known, the key for recovering the code is the value of the perturbation and the
to numbers labelling the element that has been distorted, in this case (1,5). Of
course, rather than distorting one matrix element we may wish to distort a random
number of them. In such a case the decoding key becomes a string of ordered
pairs of natural numbers, indicating the elements that were randomly selected to
be distorted and the corresponding values of the perturbations. Moreover, to avoid
attacks of the type known plaintext attack [4,5], in which the attacker is supposed to
have collected correctly decrypted message in order to use them to decrypt others,
one can proceed as follows: maintaining one perturbation secret as the key for
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Code numbers
0.43596704982551
0.82124392828478
0.66471591347310
0.64554449242809
0.84398334243978
reconstruction
0.43596704982551
0.82124392828478
0.66471591347310
0.64554449242809
0.84398334243978
disregarding perturbation
-0.04525907549619
0.18665860768833
0.19297513576254
0.08172338626606
-0.14994519073447
Table 1: 5 code numbers and their reconstruction. Assuming the perturbation to
be known (second column) and otherwise (third column)
decryption, other perturbations are made public and are different for every message.
This avoids the repetition of the encoding operator with the same key. Thus, the
knowledge of decrypted messages does not provide information on the encoding
operator to encrypt other messages with the same key. This prevents thereby the
possibility of known plaintext attacks.
We would like to stress that the success of the proposed procedure strongly de-
pends on the use of the identical numerical method to obtain the same basis of
null(ˆG) in the encoding and decoding process. Nevertheless, the procedure does not
depend on the machine processor. In the examples presented here the encoding was
performed in powerful computer cluster, and the decoding in a laptop, using Matlab
6.5. Let us also remark that only the precision in the representation of operatorˆG
is crucial for the code reconstruction, since the numerical errors in determining |pc?
are not magnified. This is due to the fact that, sinceˆU†ˆU is the identity operator
in RN−(M+1), the inverse recovering of |q? from |q? =ˆU|pc? is very stable against
perturbations of |pc?.
3 Conclusions
An encoding/decoding scheme for embedding hidden information into the statistic
distribution of a physical system has been presented. The encryption security is
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based on the extreme instability of the encoding process, which is endowed with the
following feature: a tiny perturbation to the matrix yielding the eigenvalues used to
construct the encoding operator produces a huge effect in the recovery process.
Thus, the key for retrieving the code is given by the value of the perturbation.
The security for interchanging the key is, of course, essential, but we can rely on the
secure quantum key distribution protocol to ensure a safe key delivery. Conversely,
the quantum protocol can make use of the proposed setting for encryption, as it
entails the transition of very little information through the quantum channel.
A remarkable property of making use of a statistical distribution for the purpose
of storing encrypted information is the fact that, the larger the dimension of the
distribution is, the larger the amount of encrypted information that can be stored.
This opens the possibility of devising more sophisticated encryption algorithms than
the one advanced here, yet based on the same principles.
We believe that the results we have presented are certainly encouraging and feel
confident that they will contribute to many fruitful discussions and follow-up work
in the subject. Finally we would like to stress that the proposed scheme is not
restricted to be applied only on physical distributions. When a physical distribution
is involved, one has also stored the information on the physical system. Hence
the importance of using an appropriate entropic measure for the encoding/decoding
process. If the entropic measure is the right one, after recovering the statistical
distribution it can be used to make correct predictions on the expected values of
physical quantities which are not experimentally available.
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