Secure Image Transmission for Cloud Storage System Using Hybrid Scheme

International Journal of Engineering Research and Development
e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com
Volume 11, Issue 09 (September 2015), PP.18-26
18
Secure Image Transmission for Cloud Storage System
Using Hybrid Scheme
Shelly1
, Dr Rajesh Kumar Bawa2
1
Student of Punjabi University, Patiala.
2
Proff. Of Punjabi University, Patiala.
Abstract :- Data over the cloud is transferred or transmitted between servers and users. Privacy of that
data is very important as it belongs to personal information. If data get hacked by the hacker, can be
used to defame a person’s social data. Sometimes delay are held during data transmission. i.e. Mobile
communication, bandwidth is low. Hence compression algorithms are proposed for fast and efficient
transmission, encryption is used for security purposes and blurring is used by providing additional
layers of security. These algorithms are hybridized for having a robust and efficient security and
transmission over cloud storage system.
Keywords:- Secure Image Transfer, Encryption, Blur Map Model, Compression.
I. INTRODUCTION
Every user require secure storage and safe data transmission. Several trends are there in cloud
computing, which are an internet based deployment. The more powerful processors together with the
(SaaS) software as a service computing architecture, transforming data centers into pools of service on
large scale. To subscribe high quality services we have to increase network bandwidth, yet flexible
connections can be made. As we know that data security is an important term of (QOS) quality of
service, cloud computing has to invent new challenging security threats for number of reasons. firstly,
traditional cryptographic primitives are the most basic blocks which are used to build cryptographic
protocols and security. various kind of user’s data is stored on cloud and demand for secure and safe
data for long term and verifying the correctness of that data in the cloud becomes even more
challenging. Secondly, cloud computing is not just a third party data warehouse. The data stored in the
cloud is continuously changing by including insertions, deletion , modification , appending, recording .
basically the stored data is frequently updated by users. It is the paramount important to ensure the
correctness of data under such dynamic data updates. However, this dynamic feature also makes the
traditional integrity insurance technique futile and entails new solutions. User’s data is redundantly
stored on multiple physical locations to further reduce the data integrity threads. Last, but not the
least, deployment of cloud computing is powered by data centers running in the cooperated,
simultaneous and distributed manner. Here, distributed are used to ensure the correctness of data in
cloud for achieving the secure and robust cloud data storage system in real world.
II. SECURITY ON IMAGES
Our first goal in this project is the image compression. Various compression schemes have been
studied under the first objective. The major compression schemes evaluated under the preliminary study for this
research are DFT (Discrete Fourier Transformation), DCT (Discrete Cosine Transformation) and DWT
(Discrete Wavelet Transformation) because of their popularity and effectiveness.For images, the JPEG images
are taken into account as it preferred DWT over DCT or DFT. In DFT,[6][7] execution time is lower and it
provides lower compression as compare to the other techniques. In DCT is simple compression algorithm,
because computation count in this algorithm is limited, hence provides lower compression ratio. DWT on the
other hand, is complex and computation count is very high and it provides higher compression ratio as
compared to later two and also proven to be more effective. In wavelet transform system the entire image is
transformed and compressed as a single data object rather than block by block as in a DCT based compression
system. It can provide better image quality than DCT, especially on higher compression ratio. After preliminary
study of literature based on these compression techniques we evaluated that DWT with HAAR Wavelet is the
best performer among all other compression techniques available in our selection in terms of compression ratio
and elapsed time. Finally, the decision is made to use DWT for its effectiveness and robustness over DCT and
DFT.[6][7].

Secure image transmission for cloud storage system using hybrid scheme
19
2.1 Image Compression Using DWT
When an image has been processed of the DWT, the total number of transform coefficients is equal to
the number of samples in the original image, but the important visual information is concentrated in a few
coefficients. To reduce the number of bits needed to represent the transform, all the sub bands are quantized.
Quantization of DWT sub bands is one of the main sources of information loss. In the JPEG2000 standard, the
quantization is performed by uniform scalar quantization with dead-zone about the origin. In dead-zone scalar
quantizer with step-size j, the width of the dead-zone is 2j as shown in Figure below. The standard supports
separate quantization step-sizes for each sub band. The quantization step size j for a sub band j is calculated
based on the dynamic range of the sub band values. The formula of uniform scalar quantization with a dead-
zone is
( , )
( , ) sign( ( , ))
j
j j
j
W m n
q m n y m n
 
  
  
where Wj(m,n) is a DWT coefficient in sub band j and j is the quantization step size for the subband j. All the
resulting quantized DWT coefficients qj(m,n) are signed integers.
After the quantization, the quantized DWT coefficients are then use entropy coding to remove the coding
redundancy.
2.2 Image Encryption using Blowfish
To perform the encryption in the second object, blowfish encryption algorithm is used to hide the
image details of hidden object.[1,3-4,8] A significant number of research papers on the performance evaluation
and work flow of encryption algorithms has been studies under the literature survey part. The AES and Blowfish
algorithms were selected in the final short listing of encryption algorithms, because these two provide the best
encryption security. Out of the two shortlisted ones, the conclusion was obtained that the blowfish encryption
algorithm is considered the fastest one among the all other options. [14] Blowfish encryption algorithm is
designed in a customized way to work with images in MATLAB environment. The algorithm code is designed
to perform various rounds of encryption. The encryption algorithm is used here to hide the image details and to
create a new image with dizzy image details. The image details are made hidden in chaotic way to create a new
image with less number of details. The image is not made completely unreadable because it provokes the
hacker to crack into the encryption, whereas a low resolution less detail encryption can be easily mistaken as a
bad image.. The decryption process is the reverse process, which is used to obtain the original image by using
the reverse engineering of the cryptographic process on the receiver’s end. For the decryption, user has to enter
the same key as it was entered on the sender’s side while encrypting the image. The decryption process returns
the full resolution original image from the encrypted image once the process is complete. The image encryption
using blowfish process has been listed in the figure below(see Figure 2.
Figure 2 Blowfish encryption for image processing
j j j 2 j j j j
3 2 1 0 1 2 3
Figure 1 Dead-zone quantization about the origin.

20
Blowfish is a variable-length key, 64-bit block cipher. The algorithm consists of two parts: a key-
expansion part and a data- encryption part. Key expansion converts a variable-length key of at most 56 bytes
(448 bits) into several sub key arrays totaling 4168 bytes. Data encryption occurs via a 16-round Feistel
network. Each round consists of a key-dependent permutation, and a key- and data-dependent substitution. The
additional operations are four indexed array data lookups per round. Implementations of Blowfish that require
the fastest speeds should unroll the loop and ensure that all sub keys are stored in cache.
1.1.1 Sub keys:
Blowfish uses a large number of sub keys. These keys must be precomputed before any data
encryption or decryption.
1. The P-array consists of 18 32-bit sub keys: P1, P2,..., P18.
2. There are four 32-bit S-boxes with 256 entries each:
S1,0, S1,1,..., S1,255;
S2,0, S2,1,..,, S2,255;
S3,0, S3,1,..,, S3,255;
S4,0, S4,1,..,, S4,255.
Generating the Sub keys:
1. The sub keys are calculated using the Blowfish algorithm. The exact method is as follows:
2. Initialize first the P-array and then the four S-boxes, in order, with a fixed string. This string consists of the
hexadecimal digits of pi (less the initial 3). For example:
P1 = 0x243f6a88, P2 = 0x85a308d3, P3 = 0x13198a2e, P4 = 0x03707344
2. XOR P1 with the first 32 bits of the key, XOR P2 with the second 32-bits of the key, and so on for all bits of
the key (possibly up to P14). Repeatedly cycle through the key bits until the entire P-array has been XORed
with key bits.
3. Encrypt the all-zero string with the Blowfish algorithm, using the sub keys described in steps (1) and (2).
4. Replace P1 and P2 with the output of step (3).
5. Encrypt the output of step (3) using the Blowfish algorithm with the modified sub keys. 6. Replace P3 and P4
with the output of step (5).
6. Continue the process, replacing all entries of the P- array, and then all four S-boxes, with the output of the
continuo
There are a number of building blocks that have been demonstrated to produce strong ciphers.
 Large S-boxes: Larger S-boxes are more resistant to differential cryptanalysis. An algorithm with a 32-
bit word length can use 32-bit S-boxes.
 Key-dependent S-boxes: key-dependent S-boxes are much more resistant to these attacks differential
and linear cryptanalysis.
 Combining operations: Combining XOR mod 216
, addition mod 216
, and multiplication mod 216
+1 [7].
Key-dependent permutations: The fixed initial and final permutations of DES have been long regarded as
cryptographically worthless.
2.3 Blurring Algorithm
The proposed scheme is a modification of the one suggested by Lian et al. In their blurring algorithm,
an explicit diffusion function based on a logistic map is used to spread out the influence of a single plain image
pixel over many cipher image elements. Although the diffusion function is executed at a fairly high rate, it is
still the highest cost, in terms computational time, of the whole blurring algorithm. This is because
multiplications of real numbers are required in this stage. Table 1 lists the time required in different parts of
Lian et al’s blurring algorithm. It shows that the time for completing a single diffusion round is more than four
times longer than that for a permutation. The test is performed on a personal computer (PC) with 2.0GHz Core
i3 processor, 6 GB memory and 500GB harddisk capacity.
In our modified confusion stage, the new position of a pixel is calculated according to Eq. (1). Before
performing the pixel relocation, diffusion effect is injected by adding the current pixel value of the plain image
to the previous permuted pixel and then performs a cyclic shift. Other simple logical operations such as XOR
can be used instead of the addition operation. The shift operation can also be performed before addition.
However, simulation results found that the “add and then shift” combination leads to the best performance and
so it becomes the choice in our blurring algorithm. The new pixel value is then given by Eq. (5). [ ] ( )mod , ( ) i
= i + i−1 3 i−1 v Cyc p v L LSB v (5) where pi is the current pixel value of the plain image, L is the total
number of gray levels of the image, vi-1 is the value of the (i-1)th pixel after permutation, Cyc[s, q] is the q-bit

21
right cyclic shift on the binary sequence s, LSB3(s) refers to the value of the least three significant bits of s, vi is
the resultant pixel value in the permuted image. The seed [ ] 0, 1 v−1 ∈ L − is a sub-key obtained from the key
generator.
As the pixel value mixing depends on the value of the previously processed pixel, the operation
sequence cannot be changed. This may lead to a problem in the reversed confusion process required in
deblurring. A solution is to make the first decipher round perform the reverse position permutation only. Then
both reverse position permutation and pixel value change are performed from the second decipher round. In this
manner, an additional deblur round is required for the reverse of pixel value modification. It composes of the
simple add-and-shift operation and adds only little cost to the overall deblur procedures.
Figure 3: The step-wise security embedding model using compression, blur and encryption for the
proposed system security model to secure the new image and add it to the database
Figure 4: The step-wise reversal security embedding model using compression, blur and encryption for
the proposed system security model to reveal the secured image.

22
Index File Size
(Kb)
PROPOSED SCHEME EXISTING SCHEME
Encryption Time
(seconds)
Decryption Time
(seconds)
Encryption Time
(seconds)
Decryption Time
(Seconds)
1 1183.711 2.565694 0.646096 11.60778 10.28958
2 811.6875 1.736718 0.625936 7.973028 7.549324
3 689.6484 1.454134 0.62148 7.106211 6.66421
4 585.7813 1.236788 0.626178 5.839042 5.249151
5 930.1094 1.958269 0.623342 9.336228 8.407667
6 1481.406 3.086175 0.622886 14.74685 13.03476
7 464.9531 0.988719 0.626737 4.742196 4.296639
8 1106.797 2.322694 0.62038 11.07452 10.25855
9 339.4688 0.724391 0.623164 3.458914 3.13864
10 793.4063 1.656799 0.626932 8.34238 7.231282
11 1347.328 2.822294 0.622072 13.83517 12.14879
12 988.3672 2.083992 0.624786 10.15554 8.96825
13 987.7813 2.076051 0.6259 10.25987 9.525794
14 837.7031 1.760459 0.624406 8.473982 7.558881
15 632.4609 1.333392 0.619688 6.39154 5.502444
16 1370.156 2.880993 0.623049 13.88179 12.39352
17 861.3281 1.818985 0.625489 8.544957 7.559276
18 1215.5 2.54242 0.623796 12.44276 10.8589
19 889.875 1.896367 0.62476 9.271283 8.295494
20 827.3828 1.740709 0.623639 8.228253 7.261873
21 1276.133 2.663857 0.622935 13.1867 11.80048
22 1403.5 2.94659 0.625573 14.31333 12.92009
23 1127.906 1.074278 0.622783 5.285584 5.416851
24 883.5469 1.860694 0.623106 10.16153 7.988927
25 1825.641 3.834191 0.625366 18.813 17.10785
26 1145.063 2.398937 0.623667 11.61872 10.07544
27 792.9297 1.675993 0.625483 8.936339 6.941237
28 797.0156 1.677833 0.626425 7.910779 7.030197
29 986 2.06346 0.625672 9.986034 9.198403
30 548.4375 1.157437 0.622987 5.257926 4.652692
Table 1: The table displaying the results of improved BLOWFISH
Implementation on dataset of 50 images
All measurements were taken on a single core of an Intel Core i3-2400 CPU at 3100 MHz, and
averaged over 10000 repetitions. Our findings are summarized in Table 1 One can see that while the
initialization overhead generally has a huge impact on the performance, this effect starts to fade out already at
messages of around 256-1500 bytes. Due to the fast implementation of Blowfish algorithm, it has performed
way better than the existing AES encryption methods available. The proposed algorithm achieves nearly optimal
performance starting from 512 byte message length due to its ability of programming structure which enables it
to fully utilize the improved multiple encryption patterns and validation for its initialization overhead. The
proposed algorithm has generally performs better than the existing when configured with block size of 128-bit
and fixed s-box implementation. Also the validation method has been added to provide more flexibility and
robustness to the proposed algorithm.

23
Figure 5: The graphs of Encryption and Decryption processing speeds
[Also the average results] for proposed model
Figure 6: The graphs of Encryption and Decryption processing speeds
[Also the average results] for existing model

24
Figure 7: The graphs of Encryption and Decryption time of proposed system
Figure 8: The graphs of Encryption and Decryption time of existing system
The proposed algorithm has been proved to be way faster than the existing AES algorithm. The
existing algorithm is taking almost 2-3 times slower than the proposed algorithm. The proposed algorithm has
been proved to be efficient for both image and text data.

25
III. CONCLUSIONS
In this paper, we are proposing the compression, encryption and blurring security techniques by using
DWT , BLOWFISH and BLURMAP [ providing extra layers of security] and proposed hybridized algorithm
technique for more secure and effective security system. Blowfish is compared with AES technique which
proves that Blowfish is best encryption technique in all way.
ACKNOWLEDGEMENT
I deeply thankful to Dr Rajesh Kumar Bawa , professor , Punjabi University, Patiala. For providing his
valuable help throughout my work. I am thankful for his stimulating guidance, continuous encouragement, and
supervision. And with all hard work I completed my Research Paper.
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