Multimed Tools Appl DOI 10.1007/s11042-015-2985-7

Efficient selective image encryption Ahmed M. Ayoup 1 & Amr H. Hussein 1 & Mahmoud A. A. Attia 1

Received: 10 March 2015 / Revised: 19 August 2015 / Accepted: 5 October 2015 # Springer Science+Business Media New York 2015

Abstract Selective image encryption has a significant importance in many applications as it offers significant savings in computations, cost, and time. Many attempts are exerted for this purpose as the traditional full image encryption/decryption algorithms may be too massive. In this paper, a new technique for selective image encryption is developed. The algorithm is based on a combination between the pseudo random number sequences, Arnold permutation, and the advanced encryption standard technique. The proposed technique is intended to reduce the execution time of the encryption process and increase the robustness of the encrypted image. Keywords Pseudo random number (PRN) . Advanced encryption standard (AES) . Arnold transformation . Linear feedback shift register (LFSR)

1 Introduction The selective image encryption has a significant importance in time-critical applications wherein security is also a concern in internet banking transactions, military image database and communication, medical imaging security, satellite image security, pay-TV, confidential video conferencing, corporate communications and military, etc. To protect the images from unauthorized access, secure image transfer is required and this can be achieved by using strong encryption techniques like triple Data Encryption Standard (3DES) and Advanced Encryption Standard (AES) [12]. But the encryption of whole image using these techniques is time consuming. One way to reduce the implementation time of encryption process, improve the robustness of the encrypted image, and achieve inexpensive security is to use partial encryption (PE) or

* Ahmed M. Ayoup [email protected] Mahmoud A. A. Attia [email protected] 1

Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Tanta University, 26 Mustafa Hashim, Tanta, Egypt

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selective encryption (SE), which is the process of encrypting only carefully selected portions of the original image. Many attempts are exerted for this purpose. A novel symmetric encryption technique for medical images is presented in [7]. It uses the genetic algorithm which makes it highly adaptive. The images are segmented into a number of regions and the sensitive regions are selected on the basis of pixel intensity and entropy measurements. In addition, it uses several encryption algorithms with variable key lengths to control the execution time required for the encryption process. But, this algorithm provides lower entropy and correlation measurements than the traditional AES. Furthermore, increasing the number of regions should increase the side information which can be considered a main drawback of the algorithm. A selective encryption technique based on a combination between Arnold’s cat map, image hiding, and modified international data encryption algorithm (IDEA) technique was introduced in [10]. The selective encryption is performed by applying the IDEA to the body of the image. In this case, the body of the image is considered as the sensitive area without any rules which is considered the main drawback of this algorithm as the sensitive information may be concentrated in any region rather than the body of the image. In addition, the IDEA has low security compared to 3-DES and AES. A region based selective image encryption technique was introduced in [9]. The image is segmented into a number of regions and the sensitive areas are simply identified by user, not based on any rules. Most existing selective image encryption techniques utilize image compression [8]. The original image is first encoded by a certain kind of encoder. After that, the encoded data are classified into significant part and insignificant part according to their strength of impact or error diffusion in the decoding process. But errors in the significant part will cause substantial change in decoded output or make it unrecognizable. Cryptographic protocols are used to provide secure communication between parties. There are wide varieties of Cryptographic protocols in each network layer such as; Secure Socket Layer (SSL), Transport Layer Security (TLS), Secure Shell (SSH), Internet Protocol Security (IPSec), and Internet Security Association and Key Management Protocol (ISAKMP) to name a few. But, these proptocols are often targeted by different attacks or threats such as traditional security threats and CRN-specific security threats [2]. Many attack detection systems are introduced to combat against attacks on Cryptographic protocols such as Detection and TRAceBack (DTRAB) approach [5]. Authenticated key agreement protocol is used to protect the confidentiality, integrity, and authenticity for data transmission over insecure networks. In a network with large numbers of users, the three-party authenticated key agreement (3PAKA) protocols are preferable [16]. For cloud users, to guarantee reliable and good quality of service, storage security and computation security should be taken into consideration. For this purpose, the privacy cheating discouragement and secure computation auditing protocol, or SecCloud, which is a first protocol bridging secure storage and secure computation is presented in [14]. The aforementioned Cryptographic protocols rely upon encryption to provide secure communication between parties. Most of them utilize the advanced encryption standard (AES) technique. But applying this technique over the whole payload is time consuming. On the other hand, the introduction of the selective encryption techniques instead of full encryption using AES will save the encryption time and provide higher security and more privacy. To achieve secure and reliable communication over a network, efficient attack detection systems, storage security and computation security techniques, and selective encryption techniques should be integrated.

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In this paper, an efficient selective image encryption (ESIE) technique is presented. The proposed technique is based on a combination between the pseudo random number sequences, Arnold permutation, and the advanced encryption standard technique. It combines the randomness and good correlation properties of pseudo random number sequences, speed of arnold permutation, and robustness advantage of AES. So, the proposed ESIE technique is intended to reduce the execution time of the encryption process and increase the robustness of the encrypted image. Furthermore, no errors are encountered in the image decryption in contrast to the image compression based selective encryption techniques.

2 Proposed ESIE technique In this section, the ESIE technique is presented. Figure 1 shows the block diagram of the proposed ESIE technique. The ESIE technique follows these steps: (1) Sensitive Area Selection The plain image is divided into a number of equal size and non overlapping blocks. Entropy calculation is applied to the individual image blocks to determine the N entropy values (E1, E2, E3,….., and EN). To recognize the region of interest (ROI) or sensitive area, the blocks having the highest entropy values are selected. For small encryption time

Fig. 1 The block diagram of the proposed ESIE technique

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and high security the selected blocks should constitute 25 % of the size of the plain image. (2) Pseudo Random Number Matrix Generation This section presents the pseudo random number generation (PRNG) technique using linear feedback shift register (LFSR). Pseudo random number generators have applications in many areas where producing an unpredictable result is desirable [11]. Random number generators are very useful in developing Monte Carlo method simulations as debugging is facilitated by the ability to run the same sequence of random numbers again by starting from the same random seed. They are also used in various cryptography applications such as data and multimedia encryption keys and bank security communication channels, etc., so long as the seed is secret. Maximum-length linear feedback shift register produces an m-sequence (i.e., it cycles through all possible 2n −1 states within the shift register). The sequence of numbers generated by this method is random and the period of the sequence is (2n −1), where n is the number of shift registers used in the design or the degree of the generator polynomial P(x). The total number of internal random states generated using the LFSR are 2n −1 and depends on the order of the generator polynomial P(x). The state of the shift-register at clock pulse i is the finite length vector bi; bi ¼ ðbi ð1Þ ; bi ð2Þ ; …:; bi ðn−1Þ ; bi ðn ÞÞ

ð1Þ

and the output number ci at clock pulse i is the concatenation of these states; ci ¼ ðbi ð1Þ &bi ð2Þ & …:&bi ðn−1Þ &bi ðn ÞÞ

ð2Þ

where & denotes the concatenation operator. In this case, a sequence of pseudo random numbers ci of length M is represented as follows; C M ¼ ðc1 ; c2 ; c3 ; ……::; cM Þ

ð3Þ

For M×M input image, an M×M matrix of pseudo random numbers CM×M has to be generated. Considering a sequence of pseudo random numbers CM of length M, the rows of the coding matrix CM×M are the L-numbers successive shifted versions of the original sequence CM where C M M ði; :Þ ¼ circle shift by ðði−1Þ*LÞ of C M

ð4Þ

where i is the row index of the CM×M matrix, and L is the number of shifts. The shifted versions of the pseudo random number sequences exhibit low autocorrelation. Such important property plays a significant role in the encryption process of the input images when xored with the CM×M matrix, as it tends to increase the entropy of the encrypted image, decrease the autocorrelation between the original image and the encrypted image, and provides encrypted image with uniform histogram. (3) Image Encryption using PRN Sequences The plain image is initially encrypted using the generated PRN codes. The M×M plain image is xored with the generated PRN matrix CM×M to produce a primary PRN encrypted image. This process reduces the correlation between the plain image and encrypted image. It is also secure since it changes the histogram and provides change diffusion.

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(4) Image Arnold Transformation Arnold’s cat map scrambling technique [6, 13, 15] can be applied in image encryption, and it has more security and better effect. An image (not necessarily a cat) is hit with a transformation that apparently randomizes the original organization of its pixels. However, its period is fixed; the ACM has two main disadvantages: The original image will be returned to itself if iterating some times, and the histogram of the encrypted image is the same as the histogram of the original image. This is due to the fact that the pixel values themselves didn’t change [6]. That is the reason for applying PRN coding for the plain image before Arnold transformation. (5) AES Encryption Advanced Encryption Standard (AES) is a cryptographically secure encryption algorithm. A brute force attack requires 2128 trials for the 128-bit key size. In addition, the structure of the algorithm and the round functions used in it ensure high immunity to linear and differential cryptanalysis. Attacks against AES haven’t been successful till now and AES is the current encryption standard. The AES design can be used in any application that requires protection of data during transmission through the communication network, including applications such as electronic commerce transactions, ATM machines, and wireless communication. The AES is a universal encryption standard. It can be used for encryption of any type of data, text and other media alike [1]. In the previous section, Arnold cat map is used to shuffle the positions of the primary PRN encrypted image pixels. Selected sensitive image blocks are encrypted using AES to form the final encrypted image.

3 Statistical tests used for ESIE performance evaluation The performance of the proposed ESIE technique is evaluated using the following statistical tests: 1. Entropy The entropy H(s) of a message source m expressed in bits can be calculated as [4]. N −1

H ð mÞ ¼

2 X i¼o

Pðmi Þlog2

1 P ð mi Þ

ð5Þ

where P(mi) is the probability of the message symbol mi. For an ideal random image, the maximum value of information entropy is eight. 2. Histogram In image processing context, the histogram of an image refers to the pixel intensity values. A histogram uses a bar graph to profile the occurrence of each pixel value in an image at each different intensity value found in that image. The horizontal axis represents the pixel value. Each vertical bar represents the number of times of corresponding pixel value occurrence in the image [3]. For image encryption algorithms, the histogram of the encrypted image should be totally different from the histogram of the original image and have a uniform distribution. 3. Irregular Deviation (ID) The irregular deviation measures the quality of encryption in terms of how much the

Multimed Tools Appl Fig. 2 The 256×256 Lena image (lena256.jpg)

deviation caused by encryption is irregular [1]. The absolute of the histogram deviations HD is given by: ð6Þ

H D ðiÞ ¼ jH ðiÞ−M H j

where H is the histogram of the absolute difference between the encrypted image and the original image and MH is the mean value of this histogram. The irregular deviation DI is the calculated as follows: Table 1 Selected entropy values for N=4, 16, and 64, case and the corresponding sensitive sub-blocks

Number of image sub-blocks (N) N=4

N=16

N=64

Selected entropy values

E3=7.0908

E10=7.0921 E6=7.0611 E11=6.9925 E9=6.9137

Selected sensitive blocks

B3

B10 B6 B11 B9

E36=7.1335 E43=7.0973 E51=7.0428 E28=7.0142 E38=6.9169 E34=6.8881 E42=6.8782 E35=6.8519 E30=6.8050 E54=6.6925 E27=6.6588 E63=6.6369 E37=6.6242 E49=6.6240 E57=6.5824 E23=6.5006 B36 B30 B43 B54 B51 B27 B28 B63 B38 B37 B34 B49 B42 B57 B35 B23

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(a)

(b)

(c)

Fig. 3 a Selected sensitive sub-blocks for N=4 blocks, b selected sensitive sub-blocks for N=16 blocks, and c selected sensitive sub-blocks for N=64 blocks

255 X

Di ¼

H D ðiÞ

i¼0

M N

ð7Þ

where M and N are the dimensions of the image. The lower the value of the irregular deviation DI is, the better the encryption quality. 4. Correlation The correlation between the original image A and the decrypted image B is calculated applying the correlation formula of Eq. (8).   X X  Amn −A Bmn −B m n corr ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X X  2 X X  2  A −A B −B mn mn m n m n

ð8Þ

where Amn and Bmn are the pixel intensities or the gray scale values at a point (m, n) in the image A and B respectively. Ā and B are the mean of matrix elements A and B respectively.

Fig. 4 The block diagram of the designed n=8 stages LFSR for generating polynomial P(x) = x^8 + x^6+ x^ 5+ x^4 + 1

Multimed Tools Appl Fig. 5 The autocorrelation between the original PRN sequence and its one number successive shifted versions

1

Correlation

0.8 0.6 0.4 0.2 0 -0.2

-200

-100

0

100

200

Shift value

5. NPCR and UACI NPCR and UACI are two common measures that are used to test the influence of onepixel change on the whole image encrypted by any encryption algorithm [3]. Consider two plain images P1 and P2 have only one pixel difference. Let their corresponding ciphered images, be denoted by C1 and C2, respectively. Label the gray-scale values of the pixels at grid (i,j) in C1 and C2 by C1(i,j) and C2(i,j), respectively. Define a bipolar array, D, with the same size as the images C1 and C2. Then, D(i,j) is determined from C1(i,j) and C2(i,j). If C1(i,j) = C2(i,j), then, D(i,j)=0; otherwise, D(i,j)=1. The NPCR is defined as: X Dði; jÞ NPCR ¼

i; j

ð9Þ

W H

where W and H are the width and height of C1 or C2. The NPCR measures the percentage of the number of different pixels to the total number of pixels in these two images. The UACI is defined as:

Correlation between the PRN sequence and its 8-numbers shifted versions 1.2 1

Correlation

0.8 0.6 0.4 0.2 0 -0.2

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Shift value

Fig. 6 The autocorrelation between the original PRN sequence and its 8-numbers successive shifted versions

Multimed Tools Appl Fig. 7 Primary PRN encrypted image

" # X C 1 ði; jÞ−C 2 ði; jÞ 1 UACI ¼  100% W  H i; j 255

ð10Þ

It measures the average intensity of differences between the two images. The higher the values of NPCR and UACI are, the better the encryption. 6. Maximum Deviation Measuring Factor (MDMF) Maximum Deviation Measuring Factor (MDMF) measures the quality of encryption in terms of how it maximizes the deviation between the original and the encrypted images [1]. The steps of calculating this metric are: 1. Estimate the histogram of both original and encrypted images. 2. Calculate the absolute difference between the two histograms.

Fig. 8 Arnold permutation of the primary PRN encrypted image

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(a)

(b)

(c)

Fig. 9 The encrypted images for N=4, 16, and 64, blocks case respectively

3. Calculate the area under the absolute difference curve, divided by the total area of the image, as follows: d 1 þ d 256 X þ di 2 i¼2 255

DH ¼

!

ð11Þ

M N

where di is the amplitude of the absolute difference curve at the gray level i. M and N are the dimensions of the image to be encrypted. The higher the value of DH is, the better the quality of the encrypted image. Histogram of the plain image 800 700 600 500 400 300 200 100 0 0

50

100

150

200

250

(a)

(c)

(b)

(d)

Fig. 10 a Histogram of the plain image. b, c, and d histograms of the encrypted images for N=4, 16, and 64, blocks case respectively

Multimed Tools Appl Table 2 Measured metrics for the proposed ESIE encryption technique compared to full image encryption using AES technique Metrics

Proposed ESIE technique N=4

Encryption time (sec) Time saving (%) compared to AES Entropy Correlation between plain image and encrypted image ID

7.32

N=16 7.35

72.74 %

72.63 %

7.9973 0.0014

7.9977 0.0013

AES N=64 7.44 72.3 % 7.9978 0.0015

26.86 – 7.9972 0.0019

0.7221

0.7206

0.7227

0.7209

NPCR

99.6292

99.6246

99.6399

99.6445

UACI

27.7360

27.7787

27.7672

27.7907

0.7814

0.7836

0.7872

0.7891

MDMF

4 Simulation results The proposed ESIE encryption technique can be applied to any image format. The simulation results was implemented in MATLAB 2014a on an Intel (R), core (TM) i7-2620M, CPU@ 2.7 GHz, and 4 GB laptop running Windows 7. The 256 × 256 Lena image is used for simulations. The simulation follows these steps: 1. The 256×256 Lena image (lena256.jpg) shown in Fig. 2 is divided into equal size and non overlapping N=2m sub-blocks such that N=4, 16, and 64, for m=2, 4, and 8, respectively. The resulting sub-bloks are of sizes 128×128, 64×64, and 32×32, respectively. 2. Entropy calculation is applied to the individual image sub-blocks. 3. To recognize the region of interest (ROI) or sensitive area, the sub-blocks having the highest entropy values are selected. For small encryption time and high security the selected sub-blocks should constitute 25 % of size of input image. The selected entropy values for N=4, 16, and 64, case and the corresponding sensitive sub-blocks are listed in Table 1. Figure 3 shows the selected sub-blocks for N=4, 16, and 64, cases.

Original

Encrypted

Fig. 11 Original and encrypted images (320×320 TV image) for N=16 sub-blocks (TV.jpg)

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Original

Encrypted

Fig. 12 Original and encrypted images (64×64 Camera man image) for N=16 sub-blocks (Camera man.tif)

4. Generation of a pseudo random numbers (PRN) sequence CM using n-stages linear feedback shift register. For 256×256 plain image, it is required to generate a 256 length PRN sequence. In this case, n=8 stages LFSR is used. For maximum length sequence, the LFSR satisfies the generating polynomial P(x) of Eq. (12). It generates n=28 −1=255 internal random states. Figure 4 shows the block diagram of the designed n=8 stages LFSR. The required 256 length sequence C256 is composed of the 255 random states plus the first state. PðxÞ ¼ x8 þ x6 þ x5 þ x4 þ 1

ð12Þ

The main advantage of the PRN sequence is that it exhibits low autocorrelationn properties that enhances the encryption process. Figure 5 shows the autocorrelation between the original PRN sequence and its one number successive shifted versions. 5. 256×256 PRN coding matrix formation. The individual rows of the coding matrix C256× 256 are the 8-numbers successive shifted versions of the original sequence C256. The shift between any two adjacent PRN sequences in the coding matrix is chosen to be 8 numbers to minimize the correlation between the adjacent pixels in the same column. Figure 6

Original

Encrypted

Fig. 13 Original and encrypted images (512×512 Lena image) for N=16 sub-blocks (lena512.bmp)

Multimed Tools Appl Table 3 Measured metrics for the proposed SE encryption technique compared to full image encryption using AES technique for the three different images (TV.jpg), (Camera man.tif), and (lena512.bmp) Image size

Proposed ESIE technique

AES technique

Entropy

Entropy

Encryption time (sec)

Encryption time (sec)

(64×64) (TV.jpg)

7.9329

2.1

2.7574

1.03

(320×320) (Camera man.tif)

7.9941

21.86

7.4464

59.11

(512×512) (lena512.bmp)

7.9993

85.563

7.9993

116.24

shows the autocorrelation between the original PRN sequence and its 8-numbers successive shifted versions. After PRN coding matrix formation, the input plain image is xored with the PRN matrix to generate the primary PRN encrypted image as shown in Fig. 7. 6. Arnold cat map is used to shuffle the positions of the primary PRN encrypted image pixels, as shown in Fig. 8. 7. Selected sensitive image sub-blocks are encrypted using advanced encryption standard (AES) to form the final encrypted image. The encrypted images for N=4, 16, and 64, block case are shown in Fig. 9. The plain image histogram is non-uniform while proposed SE encryption technique results in uniform histogram in all cases, as shown in Fig. 10. The simulation results indicate that the proposed ESIE technique provides higher entropy, lower correlation between the plain image and the encrypted image, and reduced encryption time than the AES technique. The encryption times for N=4, 16, and 64, block case are reduced by 72.74, 72.63, and 72.3 %, respectively compared to full image encryption using AES technique as listed in Table 2. Furthermore, the proposed technique provides higher entropy and lower correlation between the plain image and the encrypted image compared to full image encryption using AES technique, as listed in Table 2. Further simulations are performed on three different images of different sizes using the ESIE techniques. The three images are (TV.jpg), (Camera man.tif), and (lena512.bmp) as shown in Figs. 11, 12, and 13 respectively. The measured metrics for the ESIE technique compared to full image encryption using AES technique are listed in Table 3. The ESIE technique provides higher entropy and lower execution time compared to full image encryption using AES technique, as listed in Table 3. The ESIE technique results in uniform histogram in all cases, as shown in Fig. 14.

5 Conclusion In this paper, an efficient selective image encryption technique is presented. The algorithm is based on a hybrid combination between the pseudo random number sequences, Arnold permutation, and the advanced encryption standard technique. The proposed technique is intended to reduce the execution time of the encryption process and increase the robustness of the encrypted image. The simulation results indicate that the proposed technique provides higher entropy, lower correlation between the plain image and the encrypted image, and reduced encryption time than the AES technique. The encryption times for N=4, 16, and 64, blocks case are reduced by 72.74, 72.63, and 72.3 %, respectively compared to the full image encryption using AES technique. Furthermore, the proposed technique provides higher

Multimed Tools Appl Fig. 14 a, b, and c histograms of the encrypted images; (TV.jpg), (Camera man.tif), and (lena512.bmp) respectively for N= 16 sub-blocks

(a)

(b)

(c)

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entropy and lower correlation between the plain image and the encrypted image compared to full image encryption using AES technique.

References 1. Advanced Encryption Standard (AES) (2001) Federal Information Processing Standards Publication 197 2. Attar A, Tang H, Vasilakos AV, Yu FR, Leung VCM (2012) A survey of security challenges in cognitive radio networks: solutions and future research directions. Proc IEEE 100(12):3172–3186 3. El-Ashry I (2010) Digital image encryption. M.Sc. Thesis, Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menofia University 4. El-din H, Hamdy M, Osama S (2007) An Efficient Chaos-Based Feedback Stream Cipher (ECBFSC) for image encryption and decryption. Informatica 31:121–129 5. Fadlullah ZM, Taleb T, Vasilakos AV, Guizani M, Kato N (2010) DTRAB: combating against attacks on encrypted protocols through traffic-feature analysis. IEEE/ACM Trans Networking 18(4):1234–1247 6. Lian S (2009) Multimedia content encryption. CRC Press, Taylor & Francis Group, New York 7. Mahmood A, Dony R, Areibi S (2013) An adaptive encryption based genetic algorithms for medical images. Machine Learning for Signal Processing (MLSP), IEEE International Workshop, vol., no., pp 1–6 8. Naveenkumar S, Panduranga H, Kiran (2013) Partial image encryption for smart camera. International Conference on Recent Trends in Information Technology (ICRTIT), pp 126–132 9. Ravishankar K, Venkateshmurthy M (2006) Region based selective image encryption. Computing & Informatics, ICOCI‘06. International Conference, pp 6–8 10. Riad A, Hussein A, El-Azm AA (2012) A new selective image encryption approach using hybrid chaos and block cipher. Informatics and Systems (INFOS), 8th International Conference, vol., no., pp 36–39 11. Schindler W. Functionality classes and evaluation methodology for deterministic random number generators. Anwendungshinweise and Interpretation (AIS), pp 5–11. (Dec. 1999), Retrieved Aug. 2013 12. Stallings W (2011) Cryptography and network security principles and practice, 5th edn. Englewood Cliffs, Prentice Hall 13. Wang Y, Li T (2010) Study on image encryption algorithm based on Arnold transformation and chaotic system. Intelligent System Design and Engineering Application (ISDEA), 2010 International Conference on, vol.2, pp 449–451, 13–14 14. Wei L, Zhu H, Cao Z, Dong X, Jia W, Chen Y, Vasilakos AV (2014) Security and privacy for storage and computation in cloud computing. Inf Sci 258:371–386 15. Wu L, Zhang J, Deng W, He D (2009) Arnold transformation algorithm and Anti-Arnold transformation algorithm. Information Science and Engineering (ICISE), 1st International Conference, pp 1164–1167, 26–28 16. Yanga H, Zhanga Y, Zhoua Y, Fub X, Liua H, Vasilakosc AV (2014) Provably secure three-party authenticated key agreement protocol using smart cards. Comput Netw 58:29–38

Ahmed M. Ayoup I have pre‘master’s from faculty of engineering, tanta university, 2013. B.SC of Engineering (Electronics and Communications Department), Faculty of Engineering of aviation and technology, National institute for training on the realization of civil aviation, ministry of civil aviation, May 2006.

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Dr. Amr H. Hussein received a BSc. degree in Electronics and electrical communications engineering from the Tanta University, Egypt in 2001 and received his M.Sc. and Ph.D. degrees from Tanta University in 2007 and 2012, respectively. His M.Sc. is dedicated in the VHDL implementation of both multiple access interference (MAI), and Intersymbol interference (ISI) cancellers for DS-CDMA communications using field programmable gate arrays (FPGAs). The Ph.D. is devoted to introduce new MoM/GA algorithms for different digital beamforming applications which are of main concern in the recent wireless communication systems. Also, he has introduced VHDL implementations of both DOA estimation algorithms and the fixed beamwidth electronic scanning (FBWES) algorithm. Also, the hardware implementation of the FBWES is introduced using the microstrip technology.

Dr. Mahmoud A. A. Attia Assoc. Prof.; Department of Electronics and Electrical Communications, Faculty of Engineering, Tanta University, BSc in 1979, MSc in 1978, and PHD in 1991.