many internet vendors including amazon , google , and microsoft have introduced various cloud solutions to provide computing resources , programming environments , and software as services in a pay - as - you - go manner .
for example , amazon introduces amazon elastic compute cloud ( ec2 ) which provides computing cycle as a service and google introduces google app engine ( gae ) to provide programming environments as a service [ 1 , 2 ] .
cloud has raised many concerns related to security , data leakage , and sharing of resources . as the heart of the security concern
the first one is fingerprint verification while the second field represents partial encryption of digital image based on discrete wavelet transformation ( dwt ) .
biometrics - based individual authentication schemes that apply physiological biometric such as eye recognition , fingerprint , or behavioral actions such as speech and handwriting traits are becoming progressively widespread compared to conventional systems that are depending on e - tokens ( e.g. , rsa - token ) or knowledge such as password .
conventional authentication systems can not distinguish between an adversary that illegally gains the access privileges of an honest user and the sincere user himself .
moreover , biometric authentication schemes can be more appropriate for the users since there is no password to fail to recall or token to be stolen / lost and a single biometric trait such as fingerprint can be used to access many accounts without needing to remember their passwords .
it includes a set of ridge lines , which primary flow parallel , producing a pattern .
the points of ridge lines terminate or bifurcation are named minutiae [ 57 ] whereas permitting to galton , each ridge is categorized by several minute individualities called minutiae , which may split and almost directly reunite , enclosing a small spherical or elliptical space or sometimes the autonomous beginning or termination of ridges .
ridges and valleys often run in parallel ; sometimes they bifurcate or they terminate some other times .
finally , a good quality fingerprint must contain 2580 numbers of minutiae   according to the sensor resolution and finger location on the sensor .
briefly , fingerprint technique considers a complex pattern recognition issue ; designing algorithms that have the ability to extract significant features and match them in a strong approach is fairly hard , especially in low quality fingerprint images .
there is a common fallacy that self - propelled fingerprint recognition is a completely solved issue since it became in the first level of pattern recognition applications almost fifty years ago . on the other side ,
fingerprint technique is still a challenging and a vital pattern recognition issue . as a result ,
the fingerprint verification system consists of two phases : enrollment and verification . in enrollment phase ,
the user registers his fingerprint as a template to the server who extracts features from user 's fingerprint . at the verification phase ,
the fingerprint data of a user is compared with the template stored in the server and then it detects the validity of a user .
we notice traditional fingerprint system required extra hardware and software for each user 's logging system . in this paper
, we proposed a new scheme for verifiying user without required software for main stages : input fingerprint , preprocessing image , features extraction , classification , and verification .
additionally , our proposed scheme does not need to use the extra hardware for reading user 's fingerprint for each user 's logging system .
cryptography is considered as one of the technical field that means to support security to data being communicated on information and communications structures .
it is especially beneficial in the states of monetary and user 's data , irrespective of the actuality that the user 's data is being sent over a medium or is saved on a storage device .
it supports a powerful means of validating the authenticity of data and recognizing the culprit , if the authority and integrity of the data are desecrated . because of the improvement of electronic business , cryptographic methods are hardly critical to the improvement and usage of defiance information technologies and communications networks .
dissimilar to text messages , digital image data have their special characteristics , such as bulk capacity , high correlation among pixels , and high redundancy , not to indicate that they commonly are enormous which together make conventional encryption schemes difficult to use and slow to process . the major aim of the partial encryption is to abbreviate the computational workload and performance and to obtain a good security level .
image encryption algorithms can be classified into full encryption and partial encryption depending on the sum of the encrypted data .
regrettably , the performance of encryption and decryption functions is a major concern in real - time image transmission .
time can be classified into two ranks , the first rank of encryption time and the second rank of time transferring images .
encryption and decryption functions are not fast enough to deal with the vast amount of transmitted data .
an important criterion connecting to the scheme is to reduce the image encryption size and keep quality of the image .
partial encryption method is an appropriate scheme to encrypt only the lowest segment of data to reduce the computational requirements of huge amounts of multimedia data .
it is necessary to reduce the performance of images encryption in distributed network by minimizing the sum of data to encrypt , attaining a logical security , and minimizing the computation [ 10 , 11 ] . in this paper
, we propose a new scheme for user 's fingerprint authentication , solving the issues in the ordinary setting .
second , we merge three factors ( fingerprint , partial image encryption , and dwt ) for proposing a new scheme based on user 's fingerprint verification .
third , our proposed scheme does not require extra hardware ( fingerprint scanner , e - token ) and software ( input fingerprint , preprocessing , feature extraction , classification , matching ) .
fourth , our proposed scheme has a good balance between security and performance of fingerprint verification comparing with previous schemes .
fifth , our proposed scheme contains important merits as follows : ( 1 ) it provides mutual verification between user and service provider ; ( 2 ) it accomplishes user anonymity ; ( 3 ) the service provider and the user can achieve verified session 's keys ; ( 4 ) it has several advantages such as low cost , simple integration with available infrastructure , and essay to deploy and manage in practical system .
we detail the security analysis and implementation results in section 5 , and section 6 concludes the paper .

 figure 1 explains the main idea of our proposed scheme , which consists of three components : trusted third party ( ttp ) , service provider ( sp ) , and users ( ui ) .
ttp is responsible for gaining main keys to each user valid ( ) and service provider ( ) to complete verification phase .
generally , the proposed scheme can be divided into three phases : setup , registration , and verification . in our proposed scheme
, ui will register their username ( uni = h(uni ) ) and fingerprint ( fp ) to ttp in the setup phase .
after that , ttp computes discrete wavelet transform on user 's fingerprint fp = dwt(fp ) and generates shared key ( sh ) .
then , it sends important information ( fp , sh , uni ) , ( fp , sh ) to the service provider and user , respectively .
however , user 's important information and service provider 's important information are shared in ( fp , sh ) .
after that , ui encrypts and saves his important information in any extra device that prefers it . in verification phase
, the valid user sends their username anonymity and encrypts their fingerprint fp based on ( dwt , sh ) to sp .
then , sp verifies user 's securing information to check the validity of user depended on important information that has received at setup phase from ttp . then , sp sends back challenge value to the user for ensuring validity of sp . as a result
the discrete wavelet transform is used to analyze the two - dimension image where it has the ability to divide the image into four main areas .
the present subbands are as follows : high - low ( hl ) , low - high ( lh ) , and the high - high ( hh ) . in brief , they are indicated as dwt [ 11 , 12 ] .
the wavelet analysis is permitted in using the long periods of time where the ambition is to get more accurate information from low frequency and high frequency from the shorter zones [ 912 ] .
the important data reside in the low frequency because it gives you the opportunity to get a signal of an identity .
when you go to the high frequency content , it is characterized by high accuracy .
it can be observed by the wavelet analysis for lena 's image ( see figure 2(a ) ) .  
figure 2(b ) displays the first level of the dwt [ 13 , 14 ] .
the tactic followed by the separation of the different characteristics of the signal relies on the method of gathering the energy of a few elements .
this class of dwt refers to the place of analysis via the approximation area at level j in four zones .
the histogram image means the process of distribution density of brightness and the contrast in the gray - level image [ 9 , 10 ] .
this method is applied in our approach as measure ; anyone can note enhanced ratio during this scale .  
correlation ( crr ) is a measure of sameness between the image before and after processing .
the exemplary outcomes are near the one which can be identified by the following equation [ 1619 ] : 
 ( 1 )  crr  = ( r=1nc=1m(in(r , c)in)(i0(r , c)in0 ) )  ([r=1nc=1m(in(r , c)in)2]kkkkkkkk[r=1nc=1m(in(r , c)in)2][r=1nc=1m(in(r , c)in)2])1 , 
 where in(r , c ) is the digital value of the pixel in the ( r , c ) of image before processing and in is referring to the image before processing : 
 ( 2)in=1mnr=1n  c=1m(in(r , c)).i0(r , c ) is the digital value of the pixel in the ( r , c ) of image before processing : 
 ( 3)i0=1mnr=1n  c=1m(i0(r , c ) ) , 
 where  m is height of the image , n is width of the image , and r and c are row and column numbers . the average of these correlations is used to generate the crr of the recreated color image in rgb system .
the privacy of the encrypted data with stability in time and cost effectiveness of the encryption method is the main challenge that is faced in image encryption topic .
there are many related works that referred to this challenge [ 917 ] distributing via the frequency , spatial , and the hybrid field methods in full encryption schemes .
then , they used this key matrix to encrypt gray level as well as color images .
their scheme works fine for all types of digital images excluding the images with background of the same gray scale or same color .
nien et al .   applied a hybrid encryption technique for the true image depending on the multichaotic model .
they combined the pixel - chaotic - shuffle ( pcs ) and bit - chaotic - rearrangement ( bcr ) schemes .
the first one ( pcs ) , a fast encryption scheme which can vary the locations of each pixel , is practical to fully disregard the original image outlines applying four third - order chaos like henon , lorenz , chua , and rssler chaos maps .
the second one ( bcr ) employs chaos scheme to make chaotic codes reorganization in pixels practical .
the merging of the pcr and the bcr will cause us to increase the key space of digital images and suffers from resisting the extensive attack when correlation coefficient is refers to 0.0031 .
seyedzade et al .   presented an algorithm for image encryption depending on sha-512 hash function .
the first one does preprocessing procedure to shuffle one half of digital image while the second one applies hash function to create a random number mask .
then , the mask is xored with the other section of the image that is going to be encrypted .
abuhaiba and hassan   propose a new image encryption scheme which employs amount and phase manipulation applying differential evolution ( de ) method .
their scheme has passed key out of space analysis , algebraic analysis , and key sensitivity analysis to exhibit the security of the new image encryption scheme .
munir   presents a technique that uses the arnold cat map transformation on low frequency subband of the dct transformed to encrypt image .
their essential idea for choosing the low frequency subband of the dct transformed image is certified to the truth that the human visual system ( hvs ) is more imagined to important information ( such as object , shape ) at lower frequencies compared with higher frequency information .
their scheme is represented to be strong against noise , though to some amount since the decrypted image views some attendance of noise . in this paper , we use partial image encryption based on discrete wavelet transform detection as the main factor to verify legal user in cloud computing environment .
ttp sends important information ( discrete wavelet transform of user 's fingerprint , shared key , and user 's anonymity ) to valid user and cloud service provider .
the shared key derives from discrete wavelet transform of user 's fingerprint and uses then it uses it to encrypts / decrypts image .
additionally , our proposed scheme embeds salt key with shared key to generate one time key for each user 's login . also , our work encrypts user 's fingerprint once time for each verification phase . in the performance
our presented scheme has been evidenced to achieve sturdy security with low cost and high performance compared with previous schemes .
the wide majority of modern automated fingerprint authentication schemes are minutiae ( level 2 features ) using biometric systems [ 6 , 7 ] .
the minutiae - based systems are generally implemented well with high - accuracy fingerprint images and an adequate fingerprint surface area .
these conditions , however , may not be attainable at any time . in several cases , only a small part of the test fingerprint can be comparative with the reference fingerprint .
additionally , comparing fingerprints gained via small - area sensors is hard due to the potentiality of having too little interference between different gains of the same finger .
furthermore , the image of fingerprint is a unique pattern that consists of ridge and valley points on the surface of a finger .
the ridge is well defined as a single curved section , and a valley is the region between two adjacent ridges .
minutiae points ( see figure 4 ) represent the local ridge cavities , which are classified into two classes : bifurcations and ridge endings .
continuously , these minutiae points are employed to detect the uniqueness of a user 's fingerprint .
ultimately , the minutiae consider the first part processes a raw fingerprint image and extracts fingerprint features from that image . on the other side
arora and singla   presented a scheme that depended on fingerprint verification which has been advanced using laboratory virtual instrument engineering workbench ( labview ) .
the proposed scheme uses a learning stage during the enrollment phase , which does not exist in conventional image - depending systems . in the learning stage a pseudorandom subsampling is achieved in which pixels are analyzed by testing their surrounding neighborhood for regularity and each pixel is categorized according to how large the regularity of its surrounding neighborhood is ( e.g. , 3  3 , 5  5 , etc . ) .
this stage will reduce the amount of computations in the matching phase . in the verification phase
chen et al .   presented rebuilding the fingerprint 's orientation field from minutiae and employ it in the matching phase to enhance the system 's performance .
cao et al .   have presented two novel characteristics to deal with nonlinear deformation in fingerprints .
presented incorporating ridge characteristics such as ridge count , ridge curvature direction , ridge length , and ridge type together with minutiae to increase performance of the matching phase . in this paper , we propose a secure and efficient fingerprint verification scheme for the cloud computing using partial encryption of fingerprint based on discrete wavelet transform . in this paper
, we proposed a new scheme for verifying user without required software for main stages : input fingerprint , preprocessing image , feature extraction , classification , and verification .
additionally , our proposed scheme does not need to use the extra hardware for reading user 's fingerprint for each user 's logging system .
our proposed scheme consists of three phases  setup , registration , and verification . in the setup phase
, the major elements ( ttp , sp , and ui ) also use a cryptographic hash function h( ) and a symmetric key encryption / decryption enc()/dec( ) , and ttp sets up n = pq , where both p and q are two large primes .
the user ( ui ) sends his username ( uni = h(username ) ) and fingerprint fpi to the trusted third party ( ttp ) through a secure channel .
after that , ttp computes fpi = dwt(fpi ) where dwt is used to analyze the two - dimension image where it has the ability to divide the image into four main areas .
the data area is called the low - low ( ll ) subband . occasionally , it is named as an approximation area and represents fpi. after that , ttp saves ( uni , fpi ) and then provides public parameters keys pk = ( uni , fpi , 
 sh 
  zn ) and secret keys sk = ( fpi , sh  zn ) to service provider and each user , respectively , in the secure channel .
lastly , ui encrypts his secret keys ( sk = encsh(sk ) ) by using his shared key and then saves his encrypted file at his preferred storage such as usb , samsung mobile . after registration phase
the verification session is qualified as follows ( see figure 5).(1)ui  sp : uni , fpi , ri .
ui performs the following steps.(i)decrypt his secret keys file based on his shared key sk = decsh(sk).(ii)generate random number ri  zn and compute one time anonymous username uni = h(uniri ) , once shared key sh = sh  ri , and ( fpi = encsh(fpi)).(iii)send uni , fpi , ri to sp as a first factor.(2)sp  ui : ( x , y ) , pi . 
sp checks the validity of user 's information as follows.(i)compute uni = h(uniri ) and check whether uni equals uni. if so , he accepts the user 's information request and performs the next step .
otherwise , sp terminates verification phase.(ii)sp computes sh = sh  ri , encrypts ( fpi = decsh(fpi ) ) , and checks whether fpi equals fpi. if so , he accepts the user 's information request and performs the next step .
otherwise , sp terminates verification phase.(iii)sp generates a random number from user 's fingerprint ( pi  fpi ) and positions ( x , y ) of pi as a challenge to valid user for ensuring validity of sp ( see figure 6).(3)user verifies fpi(x , y ) = ?
( i)decrypt his secret keys file based on his shared key sk = decsh(sk).(ii)generate random number ri  zn and compute one time anonymous username uni = h(uniri ) , once shared key sh = sh  ri , and ( fpi = encsh(fpi)).(iii)send uni , fpi , ri to sp as a first factor .
generate random number ri  zn and compute one time anonymous username uni = h(uniri ) , once shared key sh = sh  ri , and ( fpi = encsh(fpi ) ) .
( i)compute uni = h(uniri ) and check whether uni equals uni. if so , he accepts the user 's information request and performs the next step .
otherwise , sp terminates verification phase.(ii)sp computes sh = sh  ri , encrypts ( fpi = decsh(fpi ) ) , and checks whether fpi equals fpi. if so , he accepts the user 's information request and performs the next step .
otherwise , sp terminates verification phase.(iii)sp generates a random number from user 's fingerprint ( pi  fpi ) and positions ( x , y ) of pi as a challenge to valid user for ensuring validity of sp ( see figure 6 ) . compute uni = h(uniri ) and check whether uni equals uni. if so , he accepts the user 's information request and performs the next step .
computes sh = sh  ri , encrypts ( fpi = decsh(fpi ) ) , and checks whether fpi equals fpi. if so , he accepts the user 's information request and performs the next step .
sp generates a random number from user 's fingerprint ( pi  fpi ) and positions ( x , y ) of pi as a challenge to valid user for ensuring validity of sp ( see figure 6 ) .
user verifies fpi(x , y ) = ? pi . if it holds , then ui ensures validity of sp .
. we will show that our scheme is secure against well - known attacks such as mitm attack , replay attack , and insider attack .
moreover , our proposed scheme enjoys several merits , such as containing user anonymity , mutual verification , and session key agreement . 

proofthe mutual verification feature requires that an attacker can not impersonate a legal user to the service provider , and vice versa .
only the valid user has the secret information ( uni , fpi , ri , sh ) to send these parameters to the service provider .
sp compares ( uni , fpi ) with ( uni , fpi ) , respectively .
therefore , our proposed scheme achieves mutual verification between the two entities ( see figure 6 ) . 
 
the mutual verification feature requires that an attacker can not impersonate a legal user to the service provider , and vice versa .
only the valid user has the secret information ( uni , fpi , ri , sh ) to send these parameters to the service provider .
sp compares ( uni , fpi ) with ( uni , fpi ) , respectively .
therefore , our proposed scheme achieves mutual verification between the two entities ( see figure 6 ) . 

proofif an adversary attempts to eavesdrop on the user 's login request , he can not find the user 's anonymity from crypto hash function uni = h(uniri ) since it is embedded in a random number ri with username , which are not identified to an adversary .
if an adversary attempts to eavesdrop on the user 's login request , he can not find the user 's anonymity from crypto hash function uni = h(uniri ) since it is embedded in a random number ri with username , which are not identified to an adversary .
proofin the proposed scheme , only user 's secret data ( sk ) is stored on user 's storage such as usb , samsung phone .
the data of sk does not help an adversary to use it without the user 's shared key ( sh ) .
if the legal user loses his preferred storage , it is difficult for any adversary to access or update the credential information because he can not obtain the user 's secret key ( sk ) .
therefore , the user 's secret data protects from retrieving by an adversary . 
  in the proposed scheme ,
only user 's secret data ( sk ) is stored on user 's storage such as usb , samsung phone .
the data of sk does not help an adversary to use it without the user 's shared key ( sh ) . if the legal user loses his preferred storage , it is difficult for any adversary to access or update the credential information because he can not obtain the user 's secret key ( sk ) .
proofin our proposed scheme , when the user sends his parameters to log in to the the service provider , he generates secret key sh = sh  ri to encrypt discrete wavelet transform of his fingerprint which is represented by fpi = encsh(fpi ) . even if an adversary can access the previous session key , he is still unable to get fresh values of sh which generates once for each user 's login session .
continuously , the service provider can obtain the same new key sh = sh  ri based on secret parameters ( sh , pi ) to compute fpi = decsh(fpi ) .
finally , sp checks the validity of user 's fingerprint based on sh. additionally , our proposed scheme depends on user 's fingerprint to obtain secret keys ( sh , sh ) that are derived by using ( ( x , y ) , pi ) .
. 
  in our proposed scheme , when the user sends his parameters to log in to the the service provider , he generates secret key sh = sh  ri to encrypt discrete wavelet transform of his fingerprint which is represented by fpi = encsh(fpi ) .
even if an adversary can access the previous session key , he is still unable to get fresh values of sh which generates once for each user 's login session .
, the service provider can obtain the same new key sh = sh  ri based on secret parameters ( sh , pi ) to compute fpi = decsh(fpi ) .
finally , sp checks the validity of user 's fingerprint based on sh. additionally , our proposed scheme depends on user 's fingerprint to obtain secret keys ( sh , sh ) that are derived by using ( ( x , y ) , pi ) .
proofour proposed scheme preserves the user 's fingerprint even when the secret key sh is disclosed or leaked .
if the secret key is revealed by the adversary , the verification of the system is not impressed , and he can not use this key in the next login phase . at the same time , it is extremely hard that an adversary can derive the secret key which consists of random number and shared key sh .
an adversary still can not obtain the secret key sh which is used to encrypt user 's fingerprint fpi = encsh(fpi ) .
our proposed scheme preserves the user 's fingerprint even when the secret key sh is disclosed or leaked . if the secret key is revealed by the adversary , the verification of the system is not impressed , and he can not use this key in the next login phase . at the same time , it is extremely hard that an adversary can derive the secret key which consists of random number and shared key sh .
an adversary still can not obtain the secret key sh which is used to encrypt user 's fingerprint fpi = encsh(fpi ) .
proofan adversary performs a replay attack by eavesdropping the login message which is sent by a legal user to the service provider .
then , an adversary reuses this message to impersonate the user when logging into the system in a next session . in our proposed scheme
, each new user 's login request should be identical with sp 's keys ( uni , fpi , ri , sh ) . therefore , an adversary can not pass any replayed message to the sp 's verification .
an adversary performs a replay attack by eavesdropping the login message which is sent by a legal user to the service provider .
then , an adversary reuses this message to impersonate the user when logging into the system in a next session . in our proposed scheme
, each new user 's login request should be identical with sp 's keys ( uni , fpi , ri , sh ) .
therefore , an adversary can not pass any replayed message to the sp 's verification .
proofthis type of attack happens when a valid user submits his login message to the service provider ; the adversary tries to catch user 's message and send it ( or an updated version of the message ) back to the same user . in our proposed scheme
, the adversary fails to cheat the service provider since he can not restore the pair ( ( uni , sh ) , fp ) sent from the user via verification phase .
the service provider and user have one - time secret keys ( sh , sh ) to compute ( fpi , fpi ) , respectively .
this type of attack happens when a valid user submits his login message to the service provider ; the adversary tries to catch user 's message and send it ( or an updated version of the message ) back to the same user . in our proposed scheme , the adversary fails to cheat the service provider since he can not restore the pair ( ( uni , sh ) , fp ) sent from the user via verification phase .
the service provider and user have one - time secret keys ( sh , sh ) to compute ( fpi , fpi ) , respectively .
proofthis type of attack is intended when an adversary has the ability to intercept the messages between users and the server .
then , he uses this message when the user signs out from the server . in our proposed scheme , the factors are securely encrypted and sent to the service provider .
generation of the random value ri is through the creation of sensitive data ( sh , fp ) by ui as a login request to sp .
therefore , an adversary spotting communication between ui and sp can learn ri which is used only once ; he is unable to compute sh , fp. however , when ui signs out of the service provider , an adversary can not compute both factors to impersonate the genuine user . as a result ,
this type of attack is intended when an adversary has the ability to intercept the messages between users and the server .
then , he uses this message when the user signs out from the server . in our proposed scheme , the factors are securely encrypted and sent to the service provider .
generation of the random value ri is through the creation of sensitive data ( sh , fp ) by ui as a login request to sp .
therefore , an adversary spotting communication between ui and sp can learn ri which is used only once ; he is unable to compute sh , fp. however , when ui signs out of the service provider , an adversary can not compute both factors to impersonate the genuine user . as a result ,
proofin case of lost or stolen information of user 's preferred storage such as usb , ui will present request to sp for its revocation by pushing his information ( uni , fpi ) .
sp verifies the user 's secure information and ensures whether uni = uni and ppi = ppi. if the result is valid , sp deletes registration of user 's file ( sk = ( fpi , sh , n ) from registration table .
lastly , the user can change his username and password by reperforming the registration phase .
additionally , an adversary can not get any benefits from stealing user 's extra storage because he can not be tolerated to decrypt registration user 's file which requires from an adversary to obtain user 's fingerprint fpi .
obviously , our proposed scheme can provide revocation . 
  in case of lost or stolen information of user 's preferred storage such as usb , ui will present request to sp for its revocation by pushing his information ( uni , fpi ) .
sp verifies the user 's secure information and ensures whether uni = uni and ppi = ppi. if the result is valid , sp deletes registration of user 's file ( sk = ( fpi , sh , n ) from registration table .
lastly , the user can change his username and password by reperforming the registration phase .
additionally , an adversary can not get any benefits from stealing user 's extra storage because he can not be tolerated to decrypt registration user 's file which requires from an adversary to obtain user 's fingerprint fpi .
we conduct several experiments for gauging the efficiency and the effectiveness of our work .  
figure 7 shows time processing of verification and login phases . however , the average time for the login and verification phase of our work is equal to 0.135601 seconds for each user who indicates the high speed of our solution .
we have registered during our experiments 2500 users and supposed that each user needs maximum 0.75 seconds for logging into the system .
our proposed scheme uses casia fingerprint image database version 5.0 ( or casia - fingerprintv5 ) that consists of 20,000 user 's fingerprint images of 500 topics .
the users ' fingerprint images of casia - fingerprintv5 were taken using uru4000 fingerprint sensor in one session .
the volunteers of casia - fingerprintv5 include graduate students , employees , and waiters . in partial image encryption of discrete wavelet transform of fingerprint
, we demonstrate the experimental results of the first two users ' fingerprints of different sizes with color image . to prove the sturdiness of our proposed scheme , we have implemented statistical analysis by computing the histograms , entropy , and the correlation of two adjacent pixels in the original image / encrypted image .
the results of experimental and statistical analysis show that our proposed scheme supports an efficient and secure manner for real - time image encryption and transmission .
figures 8 , 9 , 10 , and 11 explain histogram of original , encrypted , and decrypted images .
in addition to that , table 4 shows the results of correlation and entropy of original , encrypted , and decrypted images .
in this paper , we have proposed a new fingerprint verification scheme for cloud computing environment that includes one - time username anonymity and partial image encryption based on edge detection as a second factor .
these vital merits include the following : ( 1 ) the valid user can freely choose his passwords ; ( 2 ) our proposed scheme supports mutual verification between service provider and legal user ; ( 3 ) it achieves one - time username anonymity ; ( 4 ) service provider and legal user can produce verified sessions keys ; ( 5 ) our proposed scheme can provide revocation and security of the stored data .
moreover , our scheme can resist the stolen - verifier problem , reflection attacks , replay attacks , forgery attacks , and parallel session attacks . on the other side ,
partial encryption is considered one of the most promising clarifications to reduce the cost of data protection in communication network . in this paper , we proposed a good concept of pixel value manipulation using discrete wavelet transform for fingerprint partial encryption scheme . from the viewpoint of security ,
the experimental results explain that our proposed scheme achieves better security than the individual encryption schemes .
the proposed scheme has a good performance , the highest entropy , and the lowest correlation compared to the present partial encryption schemes .
hence , better security has been supported via several theorems that are presented at security analysis section .
 now , the security of digital images is considered more and more essential and fingerprint plays the main role in the world of image . 
 furthermore , fingerprint recognition is a scheme of biometric verification that applies pattern recognition techniques depending on image of fingerprint individually . in the cloud environment , 
 an adversary has the ability to intercept information and must be secured from eavesdroppers . 
 unluckily , encryption and decryption functions are slow and they are often hard . 
 fingerprint techniques required extra hardware and software ; it is masqueraded by artificial gummy fingers ( spoof attacks ) . additionally , when a large number of users are being verified at the same time , the mechanism will become slow . in this paper , we employed each of the partial encryptions of user 's fingerprint and discrete wavelet transform to obtain a new scheme of fingerprint verification . 
 moreover , our proposed scheme can overcome those problems ; it does not require cost , reduces the computational supplies for huge volumes of fingerprint images , and resists well - known attacks . 
 in addition , experimental results illustrate that our proposed scheme has a good performance of user 's fingerprint verification . 
1. Introduction
2. Design Issues
3. Related Work
4. Our Proposed Scheme
5. Experimental Results
6. Conclusion