International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
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A Survey of Compression and Encryption Techniques for SMS*
Manoj Patil, Prof. Vinay Sahu
1
Computer Science & Engineering,RIST, Bhopal ,India; 2Computer Science & Engineering,RIST, Bhopal ,India
manoj.patil04@gmail.com, sahu.vinay@gmail.com
ABSTRACT
This Mobile communication is one of the fastest means of communication between one senders to other. Although the data send
through the mobiles via SMS is easy and fast but it involves a lot of security because the SMS message may contains some useful
data which can eavesdropped easily. Hence to secure such private data from un-authorized users, these SMS should be made
secure by applying some encryption technique. But by applying encryption technique on these SMS data increase the length of
the message, hence the wastage of bandwidth gets increase. So to utilize the bandwidth of these encrypted messages compression technique is required so that the length gets reduced and hence bandwidth gets utilized. Here in this paper a brief survey of
all the encryption and compression techniques used for the security of SMS is given. Also one of the efficient techniques is proposed in the paper for the secure transmission of SMS messages from sender to receiver.
Keywords : Component; Formatting; Style; Styling; insert (keywords)
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1 INTRODUCTION
W
ith the advancements in technology in the field of Mobiles various new softwares has been launched in the
market with various types of operating systems for various mobile platforms. These softwares provide a wide variety
of application that makes the task of the user easier. These
devices are not only fast but one of the fastest means of communication.
Transaction ID. This is a unique transaction ID for each new
request. It helps us solve idem potency.
User
1.
Request from client
Short Message Service (SMS)
Message Frame Structure
SI
D
Enc
(TID
Request Forward
PI
N
REQTYPE
REQDETAILS)
MACVALUE
The choice of encrypting the account number is left to the implementation. However one must note that by increasing the
number of fields in the encryption string the chances of message length crossing 160 characters increases.TID specifies the
Copyright © 2013 SciResPub.
Reply from server
Server
Query for services
A frame structure to the request and reply messages is essential to parse the messages easily. The field that can be used in a
request message include
TI
D
reply on port 50000
Data Center
Short Message Service (SMS) is a text messaging service component of phone, web, or mobile communication systems, using standardized communications protocols that allow the
exchange of short text messages between fixed line or mobile
phone devices [1].
ACCOU
NT
NUM
BER
RELATED WORK
Reply from external server
Additional Services
Figure1. Flow of control in mobile communication
LZW COMPRESSION
The original Lempel Ziv [13] approach to data compression
was first published in 1977, followed by an alternate approach
in 1978. Terry Welch's refinements to the 1978 algorithm were
published in 1984 [14]. The algorithm is surprisingly simple. In
a nutshell, LZW compression replaces strings of characters
with single codes. It does not do any analysis of the incoming
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
text. Instead, it just adds every new string of characters it sees
to a table of strings. Compression occurs when a single code is
output instead of a string of characters.
The code that the LZW algorithm outputs can be of any arbitrary length, but it must have more bits in it than a single
character. The first 256 codes (when using eight bit characters)
are by default assigned to the standard character set. The remaining codes are assigned to strings as the algorithm proceeds. The sample program runs as shown with 12 bit codes.
This means codes 0-255 refer to individual bytes, while codes
256-4095 refer to substrings.
HUFFMAN ENCODING
Huffman encoding leverages the uneven distribution of readings in datasets, using fewer bits to encode more common
readings to achieve an overall compression. An imbalanced
tree structure is generated based on the presumed frequencies
of each reading, with high frequency readings at shallower
leaf nodes than those occurring less often. Each reading’s code
is determined by traversing the tree from root to leaf, with
each branch node providing one bit to the code. The length of
each code is therefore determined by the depth of its leaf.
460
readings on the shallower levels to minimize their code
lengths [15]. This adaptability can potentially provide higher
compression performance across all of the static and dynamic
variables without additional programming and deployment
effort. However, as discussed in Section 6, the
performance of adaptive Huffman encoding in a sample BSN
application is limited due to a number of factors.
While this adaptive technique is significantly more
computationally complex than static Huffman encoding, it can
potentially be implemented in real-time on BSN embedded
processors, although the number of processor cycles (CYC in
Equation 2) required per reading may be relatively high.
While adaptive Huffman encoding includes the storage of
reading frequencies in addition to the coding tree, its total
memory requirements are often smaller than those of the static
technique. The static tree remains a fixed size in memory
throughout execution regardless of the occurrence (or lack
thereof) of certain readings. The adaptive technique can
choose to discard certain readings that have not occurred for
some time, keeping the tree size to some maximum memory
requirement and reinserting a discarded reading should it
reoccur in the future [16].
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A number of practical issues make the use of Huffman encoding a challenge for BSNs. First, Section 6 reveals that many of
the reading frequency distributions (using the accelerometerbased BSN platform and multiple sensor locations and activities) are relatively flat, thus limiting the compression capabilities of Huffman encoding. Second, while the computational
complexity of the Huffman algorithm as it is running with an
existing tree is small, the tree itself can be quite large and potentially exceed the memory constraints of many BSN embedded processors. This is especially problematic in lossless compression when every possible reading must be encoded and
must therefore have a leaf in the tree, even if that reading is
extremely rare. Finally, traditional static Huffman encoding
depends on the existence of a reading frequency distribution
to generate the tree, and the compression performance
achieved depends on how well the dynamic data conforms to
that frequency distribution. It is therefore essential that a BSN
developer wanting to use Huffman encoding perform extensive data collections to profile the reading frequency distribution. As discussed in Section 3.3, this can be extremely difficult
given the numerous static and dynamic variables. Therefore,
this static technique’s ability to perform well across many applications, wearers, sensor locations, sensor axes, and activities
is limited.
It is therefore desirable to also consider an adaptive Huffman
encoding technique that dynamically generates and alters its
tree based on the actual reading frequency distribution as it
occurs and changes. This does not require a previously constructed tree or any reading profiling. In the tree, each reading
will carry its value along with the frequency of its use. This
way, the tree can update itself, keeping the most frequent
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DEFLATE COMPRESSION TECHNIQUE
The deflation algorithm used by gzip (also zip and zlib) is a
variation of LZ77 (Lempel-Ziv 1977, see reference below). It
finds duplicated strings in the input data. The second occurrence of a string is replaced by a pointer to the previous string,
in the form of a pair (distance, length). Distances are limited to
32K bytes, and lengths are limited to 258 bytes. When a string
does not occur anywhere in the previous 32K bytes, it is emitted as a sequence of literal bytes. (In this description, `string'
must be taken as an arbitrary sequence of bytes, and is not
restricted to printable characters.)
Literals or match lengths are compressed with one Huffman
tree, and match distances are compressed with another tree.
The trees are stored in a compact form at the start of each
block.
The blocks can have any size (except that the compressed data
for one block must fit in available memory). A block is terminated when deflate() determines that it would be useful to
start another block with fresh trees.
Duplicated strings are found using a hash table. All input
strings of length 3 are inserted in the hash table. A hash index
is computed for the next 3 bytes. If the hash chain for this index is not empty, all strings in the chain are compared with the
current input string, and the longest match is selected.
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DEFLATE ALGORITHM
run=b[index]
length=0
if run=b[index+=9]
while run=b[index+length]
index++,length++
output (run,length)
}
Here we are using the LZ77 technique which contains a
LookAheadbuffer to Match the similar matchings.
Algorithm
While (LookAheadBuffer is not empty)
{
get a reference (position, length) to length
match;
if(length>0)
{
output(position,length,nextsymbol);
shift the window length+1 position along;
}
else
{
output(0,0,first symbol in lookahead buffer);
shift the window 1 position along;
}
}
After this algorithm has finished its execution, we use Huffman algorithm to become a DEFLATE Algorithm.
1.
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DECOMPRESSION ALGORITHM
}
Index=0
While(index<1)
{
run=b[index++]
length=b[index++]
output (run,length)
Elliptic curve cryptography
Elliptic curves as algebraic/geometric entities have been studied extensively for the past 150 years, and from these studies
has emerged a rich and deep theory.
Many cryptosystems often require the use of algebraic groups.
Elliptic curves may be used to form elliptic curve groups. A
group is a set of elements with custom-defined arithmetic operations on those elements. For elliptic curve groups, these
specific operations are defined geometrically. Introducing
more stringent properties to the elements of a group, such as
limiting the number of points on such a curve, creates an underlying field for an elliptic curve group. In this classroom,
elliptic curves are first examined over real numbers in order to
illustrate the geometrical properties of elliptic curve groups.
Thereafter, elliptic curves groups are examined with the underlying fields of Fp (where p is a prime) and F2m (a binary
representation with 2m elements).
An elliptic curve over real numbers may be defined as the set
of points (x,y) which satisfy an elliptic curve equation of the
form: y2 = x3 + ax + b, where x, y, a and b are real numbers.
Each choice of the numbers a and b yields a different elliptic
curve. For example, a = -4 and b = 0.67 gives the elliptic curve
with equation y2 = x3 - 4x + 0.67; the graph of this curve is
shown below in figure 1:
If x3 + ax + b contains no repeated factors, or equivalently if
4a3 + 27b2 is not 0, then the elliptic curve y2 = x3 + ax + b can
be used to form a group. An elliptic curve group over real
numbers consists of the points on the corresponding elliptic
curve, together with a special point O called the point at infinity.
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Initialization: Put all symbols on a list sorted according to their frequency counts.
2. Repeat until the list has only one symbol left:
a. From the list pick two symbols with the lowest frequency counts. Form a Huffman subtree that has
these two symbols as child nodes and create a parent
node.
b. Assign the sum of the children's frequency counts
to the parent and insert it into the list such that the
order is maintained.
c. Delete the children from the list.
3. Assign a codeword for each leaf based on the path
from the root.
E-Deflate-RLE Algorithm
The DEFLATE Algorithm that has been implemented
is not feasible for all formats of the file and takes more time
and less Compression ratio as shown in figures in next sections and also not completely portable for the Sensor Networks, So we developed an enhance algorithm named EDeflate which gives us the best compression ratio and takes
less time than DEFLATE Algorithm specifically for Body Sensor Network data. E-DEFLATE algorithm is a combination of
DEFLATE and LZW Compression Algorithm which provides
us the best utilization time and the best compression ratio.
First we have to use the DEFLATE algorithm and then
passes this DEFLATE algorithm to the below steps to make it
E-DEFLATE.
COMPRESSION ALGORITHM
index=0
while(index<1)
{
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
462
key is an exponentiation operation. Decryption using the private key is as well. Decryption using the public key, however,
would require performing the difficult inverse operation —
solving the discrete logarithm problem.
The discrete logarithm problem, using the values in the equation above, is simply finding x given only y, g and p.
Expanding that thought slightly: someone has multiplied g by
itself x times, and reduced the result into the field (performed
the modulo operation) as often as necessary to keep the result
smaller than p. Now, knowing y, g and p, you're trying to find
out what value of x they used.
It turns out that for large enough values of p, where p is
prime, this is extraordinarily difficult to do — much more difficult than just finding y from g, x and p.
Elliptic curve signcryption schemes
In this paper, we follow a common practice to call the group
an elliptic curve over GF(p). The addition on an elliptic curve
only involves a few arithmetic operations in GF(p), and hence
is efficient. Taking an elliptic curve E on GF(p) with P >3 as an
example, that addition follows the rules specified below:
(1) O + O = O.
(2) P + O = P for all P = (x, y) ∈ E. Namely, E has O as its identity element.
(3) P + Q = O for all P = (x , y) ∈ E and Q = (x, − y)
Namely, the inverse of (x , y) is simply (x , −y).
(4) Adding two distinct points for all P = (x1, y1) ∈ E and
Q = (x2, y2) ∈ E with x1 = x2, P + Q = (x3, y3) is defined by
x3 = λ2 − x1 − x2,
y3 = λ(x1 − x3) − y1,
where λ = (y2 − y1)/(x2 − x1).
(5) Doubling a point—for any P = (x, y) ∈ E with
y = 0,
2P = (x*, y*) is defined by
x* = λ2 − 2x,
y* = λ(x – x*) − y,
where λ = (3x2 + a)/(2y).
ElGamal public key encryption and digital signature schemesan
d their variants can all be extended to elliptic
curves in a
straight forward way. The elliptic curve DSS will be called
ECDSS, and its two shortened
versions
SECDSS1
and
SECDSS2, respectively. Note that in the computation of
r = (vG) mod q with ECDSS, vG = K which is a point on an
elliptic curve
is viewed as an integer. Similarly, in
r = hash(vG, m) with SECDSS1 and SECDSS2, Vg is viewed as
a binary string. Also note that instead of vG one may involve
only its x-coordinate in the computation of r, as the ycoordinate carries essentially only one bit of information and
hence may be excluded.
In this paper, we use physiological value in signcryption
scheme that is based on elliptic curve. This scheme provides
confidentiality, authentication, integrity, unforgeability and
non-repudiation. In addition, our scheme saves great amount
of computational cost especially for sender. The lower computation cost make our scheme can be applied to the lower computational power device like mobile device more efficiently.
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Elliptic curve discrete logarithms
The discrete log problem is to find logarithm of a number
within a finite field arithmetic system.
Prime fields are fields whose sets are prime — that is, they
have a prime number of members. These are of particular interest in asymmetric cryptography because, over a prime field,
exponentiation turns out to be a relatively easy operation,
while the inverse — computing the logarithm — is very difficult. To generate a key pair in the discrete logarithm (DL) system, we have to calculate:
y=(gx)mod p
Where p is a large prime — the field size. x and g are smaller
than p. y is the public key. x is used as the private key. In Diffie
Hellman, again, the operations we wish to make 'easy', or tractable, we harness to the operation in the field which is (relatively) easy — exponentiation. So encryption using the public
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Elliptic Curve Digital Signature Algorithm (ECDSA)
ECDSA is used to create a digital signature of data (a file for
example) in order to allow you to verify its authenticity without compromising its security. ECDSA is used with
a SHA1 cryptographic hash of the message to sign (the file).
A hash is simply another mathematical equation that we apply
on every byte of data which will give us a number that is
unique to your data. Like for example, the sum of the values of
all bytes may be considered a very dumb hash function. So if
anything changes in the message (the file) then the hash will
be completely different. In the case of the SHA1 hash algorithm, it will always be 20 bytes (160 bits). It’s very useful to
validate that a file has not been modified or corrupted, you get
the 20 bytes hash for a file of any size, and you can easily recalculate that hash to make sure it matches. What ECDSA
signs is actually that hash, so if the data changes, the hash
changes, and the signature isn’t valid anymore.
For ECDSA, we first need to know our elliptic curve parameters that are (a, b, p, N and G). we already know that ‘a‘ and
‘b‘ are the parameters of the curve function (y^2 = x^3 + ax +
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
b), that ‘p‘ is the prime modulus, and that ‘N‘ is the number of
points of the curve, but there is also ‘G‘ that is needed for ECDSA, and it represents a ‘reference point’ or a point of origin if
we prefer. Those curve parameters are important and without
knowing them, we obviously can’t sign or verify a signature.
Verifying a signature isn’t just about knowing the public key,
we also need to know the curve parameters for which this
public key is derived from.
So first of all, we will have a private and a public key. The private key is a random number that is generated, and the public
key is a point on the curve generated from the point multiplication of G with the private key. We set ‘dA‘ as the private key
(random number) and ‘Qa‘ as the public key (a point), so we
have : Qa = dA * G (where G is the point of reference in the
curve parameters).
ECDSA generates a pair (R, S) together is our ECDSA signature. we create these two values in order to sign a file. First we
must generate a random value ‘k‘, and use point multiplication to calculate the point P=k*G. That point’s x value is called
‘R‘. Since the point on the curve P is represented by its (x,
y) coordinates, we only need the ‘x‘ value for the signature,
and that value will be called ‘R‘. Now all we need is the ‘S‘
value.
To calculate S, we must make a SHA1 hash of the message,
that we will consider as a very huge integer number and we’ll
call it ‘z‘. Now we can calculate S using the equation :
S = k^-1 (z + dA * R) mod p
Note here the k^-1 which is the ‘modular multiplicative inverse‘ of k. It’s basically the inverse of k, but since we are dealing with integer numbers, then that’s not possible, so it’s a
number such that(k^-1 * k ) mod p is equal to 1. Now that we
have signature, we want to verify, it’s also quite simple, and
we only need the public key (and curve parameters of course)
to do that. we use this equation to calculate a point P :
P= S^-1*z*G + S^-1 * R * Qa
If the x coordinate of the point P is equal to R, that means that
the signature is valid, otherwise it’s not.
vents SMS data from various attacks such as man-in-themiddle attack and various reply attacks. The paper also discusses the best algorithm on GSM network which is unbreakable till the publish of the paper is AES technique.
Cheng-Kang Chu, Wen Tao Zhu proposed technique regarding secure subscription of mobile in sensor-encrypted data [4].
In SMS-SED, a node or a mobile device stores a secret key of
size independent of the total number of sensor nodes and time
periods. We evaluated the feasibility of deploying 2000 nodes
for 4096 time periods at 1024-bit of security as a case study,
studied the trade off of increasing the storage requirement of a
node to significantly reduce its computation time, and provided formal security argument in the random oracle model.
Johnny Li-Chang Lo, Judith Bishop, J.H.P. Eloff proposed a
new protocol called SMSSec which is used as a end-to-end
protocol for the secure transmission of SMS in mobile communication [5]. The paper provides securing of the SMS data
by encrypting the message but here in this paper the limitations of the encryption is discussed such as the size of the
message length is increased and is inexpensive. So an end-toend protocol is implemented which not only provides encryption of the SMS but also removes the limitations of the varying
size of the SMS text message after encryption.
Muhammad Fahad Khan, Saira Beg and Humayun Rehman
implemented a new technique of transmission of compresses
audio through SMS [6]. SMS can’t be used for transfer of audio
data since SMS may contain less bandwidth. Hence the idea is
to compress the audio signal data into text and compresses so
that it will not take much of the bandwidth and data is transferred to the receiver through SMS.
Jung-San Lee, Ya-Fen Chang and Chin-Chen Chang proposed
a new technique of authentication in mobile communication
[7]. As we know that the most of the private data transmission
can be done through mobiles, hence security plays an important role during the transmission of these messages. There
are various authentication techniques available for the security
of these messages in the mobiles. The technique implemented
here is an efficient technique for the mobile commerce.
Anita Singhrova, Dr. Nupur Prakash provides a new innovation in the field of mobile communication. Here in this technique performance analysis of various security protocols in
mobiles has been proposed [8]. Here in this paper various encryption and authentication technique needed during the
transmission of data from mobiles has been implemented. The
analysis is on the basis of system performance related to the
overhead cost and computational time complexity of the 2G
and 3G network.
Stephan Rein, Clemens Guhmann implemented compression
of text data in mobiles [9]. Here in this paper provides a low
complexity based arithmetic coding compression of the text
transferred through Mobiles. The proposed technique implemented here takes RAM memory size of 128 kBytes and the
loseless compression technique can be used in wired and wireless networks.
Abu Shamim Mohammad Arif implemented an enhanced
static data compression on short messages [10]. Here in this
paper a new technique of data compression on Bengali short
messages for the small devices using the concept of masking
and dictionary. The technique provides the asking of each
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2 LITERATURE REVIEW
Tarek M. Mahmoud proposed an efficient technique of hybrid
compression and encryption for securing of SMS in symbian
operating system [2]. The technique implemented here uses
the compression and encryption technique so that SMS send
through mobiles can be made secure. Here in this paper RSA
based encryption technique is used so that the chances of
eavesdropping have been reduced but encryption increases
the size of the text message, hence bandwidth is not utilized.
So to overcome this limitation a new technique of compression
and encryption is implemented so that the data is secure and
bandwidth gets utilized.
Neetesh Saxena, Narendra S. Chaudhari proposed a new
technique of securing SMS in GSM Network [3]. Here in this
paper a comparison of different encryption techniques such as
DES, Triple-DES, AES, Blowfish algorithm and different signature techniques such as DSA or RSA has been implemented.
The combinatorial method of encryption and signature preCopyright © 2013 SciResPub.
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International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013
ISSN 2278-7763
character in the messages and a dictionary is mentioned for
each character in the message and then using associative rule
of data mining compression takes place. The technique implemented here is an efficient technique especially for the
short Bengali messages.
Iwan Handoyo Putro, Petrus Santoso proposed a new technique of data compression which is based on arithmetic encoding [11]. Here in this paper the limitations of the message
length is presented and a solution of how we can send more
than 160 characters in the message.
Cleonilson Protásio de Souza has proposed a new technique of
SMS compression using the concept of Electrocardiogram [12].
In order to develop a mobile ECG instrument based on SMS
and take advantages of its extremely low cost, in this work
was present an efficient compression method to compress the
ECG signal into a SMS message. The compression method is
based on Huffman-ASCII coding and the experimental results
shown that with it is possible to allows mobile phone to
transmit ECG signal via SMS.
In 2012 a new way of encryption the text in SMS in Android
operating systems has been proposed [17]. Here in this paper
various encryption techniques such as AES, DES and RC4 but
on mobile operating system. Here the comparison of different
encryption techniques for encrypting mobile SMS has been
given.
3 CONCLUSION
464
Authentication”,IJS 2006.
[9] Stephan Rein, Clemens G¨uhmann, Frank H.P. Fitzek,”
Low-Complexity Compression of Short Messages”, 2006.
[10] Abu Shamim Mohammad Arif, Asif Mahamud, Rashedul
Islam,” An Enhanced Static Data Compression Scheme of Bengali Short Message”,IJCSIS 2009.
[11] Iwan Handoyo Putro1, Petrus Santoso2, Maya Basoeki3,”
A Short Text Compression Scheme based on Arithmetic Coding”,2007.
[12] Cleonilson Protásio de Souza , Tiago Pontes Pereira,
Raimundo C. Silvério Freire,” Electrocardiogram By Mobile
Phone: A Compression Method for Sms”,2009.
[13] J. Ziv and A. Lempel, "A Universal Algorithm for Sequential Data Compression", IEEE Transactions on Information
Theory, May 1977.
[14] Terry Welch, "A Technique for High-Performance Data
Compression", Computer, June 1984.
[15] Lu, W. and Gough, M., “A Fast-adaptive Huffman Coding
Algorithm,” IEEE Transactions on Communications, vol. 41,
no. 4, pp. 535-538, 1993.
[16] Sadler, C.M. and Martonosi, M., “Data Compression Algorithms for Energy-constrained Devices in Delay Tolerant Networks,” ACM proceedings of the international conference on
Embedded Networked Sensor Systems, pp. 265-278, 2006.
[17] Rohan Rayarikar, Sanket Upadhyay, Priyanka Pimpale, “
SMS Encryption using AES Algorithm on Android”,2012.
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Although there are various encryption and compression techniques implemented so far in various applications. But here in
this paper a survey of all the techniques implemented so far in
encryption and compression as well as in mobile operating
systems has been implemented. Here this time the compression and encryption operation is performed on SMS to provide
security and less bandwidth in sending SMS is proposed.
REFERENCES
[1] The Text Message Turns 20, CNN, December 3, 2012.
[2] Tarek M. Mahmoud, Bahgat A. Abdel-latef, Awny A. Ahmed,” Hybrid Compression Encryption Technique for Securing SMS”, IJCSS), Volume (3): Issue (6).
[3] Neetesh Saxena, Narendra S. Chaudhari,“A Secure Approach for SMS in GSM Network”,ACM 2012.
[4] Cheng-Kang Chu, Wen Tao Zhu, Sherman S. M. Chow,”
Secure Mobile Subscription of Sensor-Encrypted Data”,ACM
2011.
[5] Johnny Li-Chang Lo, Judith Bishop, J.H.P. Eloff,” SMSSec:
An end-to-end protocol for secure SMS”, 2008 Elsevier.
[6] Muhammad Fahad Khan, Saira Beg and Humayun
Rehman,” Transference of Compressed Audio through SMS
Using Prediction by Partial Matching Technique”, 2012.
[7] Jung-San Lee, Ya-Fen Chang and Chin-Chen Chang,” Secure Authentication Protocols for Mobile Commerce Transactions”, 2008.
[8] Anita Singhrova, Dr. Nupur Prakash,” Performance Analysis of Mobile Security Protocols: Encryption and
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