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RE
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As
p
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te
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5
State
o
f
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it
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telli
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m
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ev
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.
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liv
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f
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p
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ig
d
ata,
w
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lab
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ased
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p
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p
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H
o
w
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v
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as
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iza
tio
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b
eg
i
n
to
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t
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.
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f
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s
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th
at
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f
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m
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d
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t
n
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d
tr
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m
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f
o
r
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.
An
y
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q
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ir
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m
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g
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if
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ac
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ch
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ev
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m
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t
w
h
ich
ad
v
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ce
s
d
ev
elo
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m
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[
3
]
,
[
4
]
.
R
elev
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n
t,
ti
m
el
y
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d
tr
u
s
t
wo
r
th
y
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ata
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es
s
en
tia
l
f
o
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s
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cc
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s
.
I
n
f
o
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at
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B
ig
Dat
a
Qu
al
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m
p
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y
o
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g
a
n
izatio
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o
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ap
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ata
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le
v
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in
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h
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o
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.
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h
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k
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ip
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ize,
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tag
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co
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d
ef
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p
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m
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o
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o
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f
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tica
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ig
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alit
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,
y
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ata
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I
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llab
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ce
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ata
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l
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ls
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c
h
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n
f
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r
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B
ig
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Qu
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ic
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et
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ased
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ta
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d
p
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f
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v
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s
atil
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o
f
th
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to
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ls
a
llo
w
b
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s
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n
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I
T
to
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llab
o
r
ate
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q
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ick
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y
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ata
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s
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s
,
ea
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esi
g
n
an
d
ap
p
l
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b
u
s
i
n
ess
r
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s
a
n
d
p
o
licies,
as
w
ell
as
p
r
o
ac
tiv
el
y
mo
n
ito
r
t
h
e
d
ata
q
u
alit
y
p
r
o
ce
s
s
[
3
]
,
[
4
]
.
I
n
f
o
r
m
a
ti
ca
A
n
al
y
s
t
is
a
s
i
m
p
le
to
-
u
tili
ze
,
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r
o
w
s
er
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ased
in
s
tr
u
m
en
t
t
h
at
en
g
a
g
es
t
h
e
b
u
s
i
n
ess
t
o
ef
f
o
r
tle
s
s
l
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ta
k
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p
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t
in
en
h
a
n
cin
g
t
h
e
n
at
u
r
e
o
f
i
n
f
o
r
m
ati
o
n
,
w
ith
o
u
t
t
h
e
r
eq
u
ir
e
m
en
t
f
o
r
I
T
in
ter
ce
s
s
io
n
.
R
ich
Set
o
f
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Qu
al
it
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r
an
s
f
o
r
m
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s
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d
Un
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v
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C
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n
n
ec
ti
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I
n
f
o
r
m
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B
ig
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u
alit
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s
h
o
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ld
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s
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co
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f
id
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to
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tak
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a
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ld
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cile
a
n
d
s
y
n
c
a
n
y
i
n
f
o
r
m
a
ti
o
n
[
5
]
.
I
t
f
ea
t
u
r
es
s
tan
d
ar
d
izatio
n
,
m
a
tch
i
n
g
,
w
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r
ld
w
id
e
ad
d
r
ess
clea
n
s
i
n
g
,
an
d
v
er
s
atile
d
ata
q
u
alit
y
m
an
ag
e
m
e
n
t
f
o
r
all
p
r
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ec
t
t
y
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h
e
p
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u
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all
y
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m
p
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w
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to
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n
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p
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co
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in
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id
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to
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a
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ce
q
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alit
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v
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v
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t
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r
e.
As
i
n
d
icate
d
b
y
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x
p
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ian
's
2
0
1
5
Data
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alit
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B
en
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ar
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R
ep
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ass
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p
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% o
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tio
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b
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co
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d
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s
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t [
5
]
.
3.
B
I
G
DA
T
A
M
ASK
I
NG
Fo
cu
s
o
n
ea
c
h
f
ield
w
it
h
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n
f
o
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m
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r
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ased
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Fo
r
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p
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to
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Evaluation Warning : The document was created with Spire.PDF for Python.
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co
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De
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h
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[
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7
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Field
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p
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r
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w
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ass
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f
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m
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p
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tectio
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Field
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ca
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co
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in
f
o
r
m
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b
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et
s
f
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tes
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[
8
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No
t
with
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co
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lar
b
u
s
i
n
es
s
r
u
les
w
i
th
r
esp
ec
t
to
:
ap
p
r
o
v
al
(
R
B
AC
)
,
s
ec
u
r
it
y
q
u
alit
y
,
r
ev
er
s
ib
ilit
y
,
an
d
ap
p
ea
r
an
ce
.
Secu
r
e
lik
e
s
e
g
m
en
ts
(
a
n
d
p
r
o
tect
r
ef
er
en
tial
r
esp
ec
tab
ilit
y
)
cr
o
s
s
w
i
s
e
o
v
er
ta
b
les
w
i
th
ca
p
ac
itie
s
attac
h
ed
to
in
f
o
r
m
atio
n
clas
s
o
r
ad
m
i
n
is
ter
lib
r
ar
ies.
T
ar
g
et
ex
is
ti
n
g
o
r
n
e
w
tab
les,
r
ec
o
r
d
s
,
ap
p
licatio
n
s
,
an
d
ev
en
c
u
s
to
m
r
ep
o
r
ts
.
Set
co
n
tr
o
ls
at
th
e
f
ield
a
n
d
e
m
p
lo
y
m
en
t
le
v
el
f
o
r
v
ar
io
u
s
b
e
n
e
f
i
ciar
ies
(
o
n
e
tar
g
et,
d
if
f
er
e
n
tial
ac
ce
s
s
)
.
R
an
d
o
m
i
za
tio
n
is
an
o
t
h
er
ap
p
r
o
ac
h
to
an
o
n
y
m
i
ze
o
r
d
e
-
d
is
tin
g
u
is
h
ac
tu
all
y
id
en
ti
f
iab
le
d
ata
[
8
]
,
[
9
]
.
F
ig
u
r
e
2
s
h
o
w
s
t
h
e
b
ig
d
ata
s
ets
m
i
g
r
atio
n
s
–
s
ec
u
r
it
y
n
ee
d
.
Fig
u
r
e
2
.
B
ig
Data
Sets
Mi
g
r
a
tio
n
s
–
Secu
r
it
y
Nee
d
Field
Sh
ie
ld
s
o
f
t
w
ar
e
in
th
e
I
R
I
Data
P
r
o
tecto
r
s
u
ite
p
r
o
v
id
es
s
i
m
p
le
ac
ce
s
s
to
n
o
n
-
r
ev
er
s
ib
le
f
u
n
ctio
n
s
f
o
r
:
R
a
n
d
o
m
d
ata
g
en
er
atio
n
-
n
o
n
-
d
eter
m
i
n
is
ti
c:
r
ep
lace
s
o
r
ig
in
al
f
ield
v
al
u
e
w
ith
r
an
d
o
m
l
y
g
en
er
ated
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ata
R
a
n
d
o
m
d
ata
s
elec
tio
n
-
r
ar
el
y
d
eter
m
in
i
s
tic:
s
p
ec
if
ic
id
e
n
ti
f
ier
s
ar
e
p
u
l
led
at
r
an
d
o
m
f
r
o
m
t
h
e
s
o
u
r
ce
,
lo
s
in
g
th
e
ir
ass
o
c
iatio
n
w
it
h
o
th
er
v
al
u
es
i
n
th
e
o
r
ig
in
al
r
o
w
.
T
h
is
d
ialo
g
i
n
th
e
I
R
I
W
o
r
k
b
en
ch
GUI
f
o
r
Field
S
h
ield
u
s
er
s
ca
n
ad
d
r
ess
an
y
s
o
u
r
ce
f
ile
f
ield
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r
d
atab
ase
co
lu
m
n
,
alo
n
g
w
it
h
o
th
er
d
ata
m
a
s
k
in
g
f
u
n
ctio
n
s
[
1
0
]
,
[
1
1
]
.
Data
s
h
u
f
f
li
n
g
i
s
also
p
o
s
s
ib
le
t
h
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g
h
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s
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r
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n
ctio
n
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m
atc
h
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y
o
u
ca
n
d
ef
in
e
i
n
th
e
I
R
I
W
o
r
k
b
en
ch
ex
p
r
ess
io
n
b
u
ild
er
.
Hash
in
g
is
a
h
ar
d
to
-
tu
r
n
ar
o
u
n
d
in
f
o
r
m
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io
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g
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y
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te
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th
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t
ch
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o
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er
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v
a
r
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le
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g
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h
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es
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g
e"
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e.
g
.
,
s
o
m
eb
o
d
y
'
s
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ec
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et
w
o
r
d
)
in
to
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led
,
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ettled
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m
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ic
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tr
i
n
g
.
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h
e
m
e
s
s
a
g
e
p
r
o
ce
s
s
,
o
r
"
h
ash
estee
m
,
"
ca
n
b
e
a
lis
t
s
ea
r
ch
u
p
f
o
r
th
e
m
e
s
s
a
g
e.
So
m
e
o
f
th
e
ti
m
e
th
er
e
is
m
o
r
e
th
a
n
o
n
e
m
e
s
s
a
g
e
f
o
r
ea
ch
r
ec
o
r
d
(
a
"
cr
ash
"
)
.
Si
n
ce
h
as
h
i
n
g
i
s
n
't
as
s
o
lid
as
en
cr
y
p
tio
n
,
o
r
as
d
ep
en
d
ab
ly
r
ev
er
s
ib
le,
it
is
at
ti
m
es
r
ea
s
o
n
ab
le
f
o
r
co
v
er
in
g
alo
n
e.
All
th
e
m
o
r
e
u
s
u
al
l
y
,
b
e
th
at
as
it
m
a
y
,
h
as
h
i
n
g
is
u
tili
ze
d
w
i
th
en
cr
y
p
t
io
n
[
1
2
]
,
[
1
3
]
.
Hash
ca
p
ac
ities
ar
e
li
k
e
w
i
s
e
u
s
ed
to
cr
ea
te
ch
ec
k
s
u
m
s
o
r
Me
s
s
a
g
e
Au
t
h
en
t
icatio
n
C
o
d
e
s
(
M
A
C
)
.
T
h
ese
ar
e
m
ad
e
an
d
s
en
t a
lo
n
g
s
id
e
m
ess
a
g
e
s
li
k
e
m
es
s
ag
e
s
,
E
FT
s
,
o
r
p
ass
w
o
r
d
s
.
A
t
th
e
p
o
i
n
t
w
h
en
th
e
m
es
s
ag
e
is
g
o
tten
,
i
ts
s
u
b
s
tan
ce
i
s
g
o
t
h
r
o
u
g
h
a
s
i
m
il
ar
h
as
h
ca
p
ac
it
y
to
m
ak
e
an
o
t
h
er
M
AC
.
I
n
th
e
ev
en
t
t
h
at
t
h
e
f
ir
s
t
a
n
d
n
e
w
M
AC
s
co
o
r
d
in
ate,
t
h
e
m
es
s
a
g
e
is
leg
it
i
m
a
te;
o
n
th
e
o
f
f
ch
a
n
ce
th
at
t
h
e
y
d
o
n
'
t,
t
h
e
m
e
s
s
a
g
e
i
s
p
r
o
b
ab
ly
g
o
in
g
to
h
a
v
e
b
ee
n
m
o
d
i
f
ied
,
a
n
d
i
n
t
h
is
w
a
y
tr
ad
ed
o
f
f
.
Utilize
t
h
e
f
ield
-
lev
e
l
h
a
s
h
in
g
ca
p
ac
ities
i
n
b
o
th
Field
S
h
iel
d
in
t
h
e
I
R
I
Data
P
r
o
tecto
r
Su
ite,
a
n
d
C
o
So
r
t
i
n
th
e
I
R
I
Data
Ma
n
a
g
er
s
u
ite,
to
h
elp
co
v
er
P
I
I
[
1
4
]
,
[
1
5
]
.
Ma
k
e
a
M
AC
f
o
r
at
leas
t
o
n
e
s
ec
tio
n
es
tee
m
s
i
n
ea
ch
l
in
e.
I
n
co
r
p
o
r
ate
it
as
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ex
tr
a
f
ield
o
r
g
iv
e
it
in
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d
if
f
er
en
t
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o
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t.
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i
lize
it
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ch
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k
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a
t
th
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i
n
f
o
r
m
at
io
n
in
t
h
e
r
ec
o
r
d
w
a
s
u
n
d
i
s
tu
r
b
ed
.
Hu
g
e
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p
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m
es sp
ee
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ie
r
th
a
n
p
ast c
y
cles [
1
6
]
,
[
1
7
]
.
B
esid
es,
ex
ec
u
tio
n
i
m
p
r
o
v
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en
ts
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n
d
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tic
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r
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g
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a
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.
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u
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Field
S
h
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ca
p
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ities
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d
ep
en
d
en
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s
in
g
E
T
L
[
1
8
]
.
3
.
1
.
D
y
na
m
ic
da
t
a
m
a
s
kin
g
D
y
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atab
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s
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it
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n
co
v
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ld
d
o
as
s
u
c
h
;
all
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in
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Sieb
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1
8
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1
9
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No
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[
1
8
]
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[
1
9
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.
Su
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s
to
:
Ma
s
k
t
h
e
s
en
s
iti
v
e
i
n
f
o
r
m
atio
n
u
n
co
v
er
ed
u
n
d
er
w
a
y
co
n
d
it
io
n
s
,
S
h
ield
cr
ea
tio
n
ap
p
licatio
n
s
an
d
d
atab
ases
w
i
th
o
u
t
ch
a
n
g
e
s
to
s
o
u
r
ce
co
d
e
,
R
esp
o
n
d
r
ap
id
ly
to
d
ec
r
ea
s
e
th
e
d
an
g
er
s
o
f
in
f
o
r
m
atio
n
r
u
p
t
u
r
es
an
d
th
e
s
u
b
s
eq
u
e
n
t
co
s
ts
,
C
u
s
to
m
ize
d
atab
ase
s
ec
u
r
it
y
f
o
r
v
ar
io
u
s
ad
m
i
n
i
s
tr
ati
v
e
o
r
b
u
s
in
e
s
s
n
ec
es
s
itie
s
.
I
n
f
o
r
m
a
tica
D
y
n
a
m
ic
Data
M
ask
i
n
g
en
co
u
r
ag
e
s
ass
o
ciatio
n
s
to
ac
h
iev
e
th
e
s
e
o
v
er
w
h
el
m
i
n
g
as
s
ig
n
m
e
n
t
s
,
p
r
o
ac
tiv
el
y
ten
d
i
n
g
to
i
n
f
o
r
m
atio
n
s
ec
u
r
it
y
ch
alle
n
g
e
s
co
n
ti
n
u
o
u
s
l
y
.
A
s
th
e
m
ai
n
g
e
n
u
i
n
e
d
y
n
a
m
ic
i
n
f
o
r
m
atio
n
co
v
er
i
n
g
ite
m
a
v
ail
ab
le,
I
n
f
o
r
m
atica
D
y
n
a
m
ic
Data
Ma
s
k
in
g
d
e
-
r
ec
o
g
n
ize
s
i
n
f
o
r
m
atio
n
a
n
d
co
n
tr
o
ls
u
n
ap
p
r
o
v
ed
ac
ce
s
s
to
g
e
n
er
atio
n
s
it
u
atio
n
s
.
I
t
h
as
m
an
y
f
o
ca
l
p
o
in
t
s
,
a
m
o
n
g
t
h
e
k
e
y
p
r
ef
er
en
ce
s
o
f
i
n
f
o
r
m
atio
n
co
v
er
in
g
,
b
o
t
h
co
n
s
ta
n
t
a
n
d
d
y
n
a
m
ic,
i
s
its
f
le
x
ib
ili
t
y
f
r
o
m
e
x
p
ec
t
in
g
c
h
an
g
es to
d
atab
a
s
es o
r
ap
p
licatio
n
s
o
u
r
ce
co
d
e
[
1
9
]
,
[
2
0
]
.
T
h
is
i
m
p
lies
v
eili
n
g
ca
n
b
e
co
n
n
ec
ted
r
ap
id
l
y
a
n
d
u
n
p
r
ete
n
ti
o
u
s
l
y
to
s
ec
u
r
e
p
r
iv
ate
i
n
f
o
r
m
atio
n
o
v
er
an
a
s
s
o
ciatio
n
,
p
a
y
i
n
g
li
ttle
m
i
n
d
to
esti
m
ate.
I
n
f
o
r
m
at
io
n
co
n
ce
al
in
g
is
li
k
e
w
is
e
g
r
an
u
lar
,
i
n
t
h
at
it
e
m
p
o
w
er
s
ass
o
ciatio
n
s
to
s
p
ec
if
icall
y
v
e
il
in
f
o
r
m
at
io
n
d
o
w
n
to
th
e
li
n
e,
s
eg
m
e
n
t,
o
r
ce
ll
lev
el.
B
esid
es,
in
f
o
r
m
atio
n
co
n
ce
ali
n
g
i
n
n
o
v
atio
n
ca
n
in
co
r
p
o
r
ate
w
it
h
ex
is
ti
n
g
v
er
i
f
icatio
n
ar
r
an
g
e
m
en
ts
,
i
n
clu
d
i
n
g
A
cti
v
eDir
ec
to
r
y
,
L
D
A
P
,
an
d
I
d
en
tit
y
A
cc
es
s
Ma
n
ag
e
m
en
t
p
r
o
g
r
a
m
m
i
n
g
.
A
l
s
o
,
it
s
u
p
p
le
m
en
ts
o
t
h
er
in
f
o
r
m
atio
n
i
n
s
u
r
a
n
ce
ad
v
an
c
es,
f
o
r
ex
a
m
p
le,
e
n
cr
y
p
tio
n
,
d
atab
ase
m
o
v
e
m
e
n
t
ch
ec
k
i
n
g
(
DA
M)
,
a
n
d
s
ec
u
r
it
y
d
ata
an
d
o
cc
asio
n
ad
m
i
n
is
tr
atio
n
(
SIE
M)
,
all
th
in
g
s
co
n
s
id
er
ed
g
iv
in
g
co
m
p
lete
in
f
o
r
m
atio
n
p
r
o
tectio
n
ass
u
r
an
ce
.
A
l
g
o
r
ith
m
-
1
B
ig
Data
P
r
iv
ac
y
Mo
d
el
Usi
n
g
Da
ta
Ma
s
k
in
g
Me
th
o
d
s
I
n
p
u
t: R
a
w
B
ig
Data
Set
s
Ou
tp
u
t: Sec
u
r
ed
B
ig
Data
Se
ts
1.
C
o
n
s
id
er
a
b
ig
d
ata
s
et
co
n
s
is
t
s
o
f
R
r
ec
o
r
d
s
B
D=
{r
1
,
r
2
,
….
.
r
n
}
2.
E
ac
h
r
ec
o
r
d
in
R
co
n
s
is
ts
o
f
s
et
o
f
co
lu
m
n
s
R
={
c1
,
c2
,
….
.
cm
}
3.
R
ec
o
r
d
Valid
atio
n
p
r
o
ce
s
s
4.
I
d
en
tify
ap
p
r
o
p
r
iate
d
ata
m
as
k
in
g
m
et
h
o
d
s
to
b
e
ap
p
lied
f
o
r
ev
er
y
co
lu
m
n
5.
DM
={
S,KR,M
,
R
,
Sh
u
f
}
6.
s
to
r
e
all
m
as
k
ed
d
ata
d
y
n
a
m
ic
all
y
7.
R
ep
ea
t step
1
to
5
f
o
r
all
th
e
f
i
les in
t
h
e
b
ig
d
ata
s
et
s
u
n
d
er
test
3
.
2
.
M
a
t
he
m
a
t
ica
l
m
o
del
T
h
e
An
o
n
y
m
it
y
o
f
t
h
e
d
ata
is
m
ea
s
u
r
ed
an
d
an
al
y
s
ed
u
s
in
g
t
h
e
f
o
llo
w
in
g
m
a
th
e
m
atica
l
eq
u
atio
n
s
,
i
n
a
b
ig
d
ata
s
et
s
k
e
y
d
e
f
i
n
itio
n
s
ar
e
d
ef
in
ed
a
n
d
m
ea
s
u
r
e
t
h
e
d
i
s
tan
ce
b
et
w
ee
n
t
h
e
k
e
y
s
to
m
a
in
tai
n
t
h
e
i
n
te
g
r
it
y
an
d
ca
lcu
l
ate
Var
iatio
n
al
d
is
ta
n
ce
u
s
in
g
E
q
u
at
io
n
(
1
)
.
[
]
∑
|
|
(
1
)
W
e
ar
e
m
a
s
k
i
n
g
t
h
e
d
atato
p
r
eser
v
e
t
h
e
p
r
iv
ac
y
a
n
d
w
e
a
n
a
l
y
ze
d
t
h
e
d
i
v
er
g
e
n
ce
f
ac
to
r
to
ch
ec
k
t
h
e
d
ata
v
alid
it
y
a
n
d
p
r
o
tectio
n
f
o
r
m
u
s
in
g
K
u
llb
ac
k
-
L
eib
l
er
(
KL
)
d
is
ta
n
ce
f
o
r
m
u
la
u
s
i
n
g
E
q
u
atio
n
(
2
)
.
[
]
∑
(
)
(
)
(
2
)
O
t
h
e
r
m
e
a
s
u
r
e
m
e
n
t
i
s
d
o
n
e
t
o
k
n
o
w
t
h
e
o
r
d
e
r
a
n
d
r
e
l
a
t
i
o
n
s
h
i
p
b
e
t
we
e
n
t
wo
a
t
t
r
i
b
u
t
e
s
wh
i
c
h
m
e
a
s
u
r
e
s
t
h
e
s
i
m
i
l
a
r
i
t
y
a
n
d
d
i
s
s
i
m
i
l
a
r
i
t
y
o
f
t
h
e
t
wo
a
t
t
r
i
b
u
t
e
s
u
n
d
e
r
p
r
i
v
c
y
p
r
o
t
e
c
t
i
o
n
,
o
r
d
e
r
d
i
s
t
a
n
c
e
b
e
t
we
e
n
t
wo
n
u
m
e
r
i
c
a
t
t
r
i
b
u
t
e
s
a
r
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m
e
a
s
u
r
e
d
u
s
i
n
g
E
q
u
a
t
i
o
n
(
3
)
.
[
]
∑
∑
(
3
)
F
o
r
C
a
t
e
g
o
r
i
c
a
l
a
t
t
r
i
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q
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NC
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[
1
]
C.
S
.
S
in
d
h
u
,
e
t
a
l
.
,
―
A
No
v
e
l
In
teg
ra
ted
F
ra
m
e
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En
s
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Big
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y
ti
c
s
o
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r
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u
d
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n
v
iro
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m
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t
‖
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In
ter
n
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l
J
o
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rn
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l
o
f
El
e
c
trica
l
a
n
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mp
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2
0
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7
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p
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2
7
9
8
-
2
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0
5
.
[
2
]
M
a
rio
Div
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,
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a
l
.
,
T
o
w
a
rd
s
a
Co
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ro
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ta
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s
‖
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ter
n
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mp
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g
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6
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2
0
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p
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3
1
6
4
-
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5
.
[
3
]
T
h
u
Ye
in
W
in
,
Hu
a
g
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ry
T
i
a
n
f
ie
ld
,
Qu
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ti
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―
Big
Da
ta
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s
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c
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A
n
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d
In
f
r
a
stru
c
tu
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s in
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u
d
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m
p
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ti
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g
‖
,
IEE
E
T
r
a
n
sa
c
ti
o
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s
o
n
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g
D
a
ta
,
v
o
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PP1
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1
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n
o
.
9
9
,
J
u
n
e
1
5
,
2
0
1
7
.
[
4
]
R
y
o
ich
iro
Ob
u
k
a
ta,
M
irald
a
Cu
k
a
,
Do
n
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ld
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m
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z
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,
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Od
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,
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rd
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ro
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li
,
―
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Ev
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lu
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ti
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f
a
n
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:
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ti
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p
p
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Di
strib
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te
d
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n
t
P
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in
g
‖
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Ad
v
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n
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d
In
fo
rm
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ti
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two
rk
in
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Ap
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AI
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2
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3
1
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ter
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,
p
p
.
2
7
1
-
2
7
5
,
2
0
1
7
.
[
5
]
Yo
u
ss
e
f
G
a
h
i,
M
o
u
h
c
i
n
e
G
u
e
n
n
o
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,
Hu
ss
e
in
T
.
M
o
u
f
tah
,
―
Big
Da
ta
A
n
a
l
y
ti
c
s:
S
e
c
u
rit
y
a
n
d
p
ri
v
a
c
y
c
h
a
ll
e
n
g
e
s
‖
,
Co
mp
u
ter
s
a
n
d
Co
mm
u
n
ica
t
io
n
(
IS
CC)
2
0
1
6
IEE
E
S
y
mp
o
siu
m
o
n
,
p
p
.
9
5
2
-
9
5
7
,
2
0
1
6
.
[
6
]
M
d
T
a
n
z
im
Kh
o
rsh
e
d
,
Ne
e
ra
j
An
a
n
d
S
h
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rm
a
,
A
a
ro
n
V
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e
k
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tt
,
A
B
M
S
h
a
w
k
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t
A
li
,
Ya
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X
ian
g
,
―
Re
a
l
T
i
m
e
C
y
b
e
r
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tt
a
c
k
A
n
a
l
y
sis
o
n
Ha
d
o
o
p
Eco
sy
ste
m
u
sin
g
M
a
c
h
in
e
Lea
rn
in
g
A
lg
o
rit
h
m
s
‖
,
Co
mp
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
(
AP
W
C
o
n
CS
E
)
2
0
1
5
2
n
d
Asia
-
P
a
c
if
ic W
o
rld
Co
n
g
re
ss
o
n
,
p
p
.
1
-
7
,
2
0
1
5
.
[
7
]
Cle
m
e
n
t
A
l
m
e
id
a
,
Ha
rsh
it
h
a
K,
M
a
n
ju
n
a
th
T
.
N,
―
A
S
tu
d
y
o
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Co
lu
m
n
S
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g
re
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a
ti
o
n
f
o
r
Da
ta
S
e
c
u
rit
y
‖
,
IJ
RCS
IT
,
v
o
l.
2
,
n
o
.
2
,
F
e
b
ru
a
ry
2
0
1
4
.
[
8
]
M
a
n
ju
n
a
th
T
.
N,
Ra
v
in
d
ra
S
He
g
a
d
i,
―
Da
ta
Qu
a
li
t
y
A
s
se
ss
m
e
n
t
M
o
d
e
l
f
o
r
Da
ta
M
ig
ra
ti
o
n
Bu
si
n
e
ss
En
terp
rise
‖
,
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
n
g
in
e
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rin
g
a
n
d
T
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c
h
n
o
l
o
g
y
(
IJ
ET
)
,
v
ol
.
5
, n
o
.
1
,
F
e
b
-
M
a
r
2
0
1
3
.
[
9
]
M
a
n
ju
n
a
th
T
.
N,
Ra
v
in
d
ra
S
H
e
g
a
d
i,
‖
S
tatisti
c
a
l
Da
ta
Qu
a
li
t
y
M
o
d
e
l
f
o
r
d
a
ta
M
ig
ra
ti
o
n
b
u
si
n
e
ss
E
n
terp
rise
‖
,
In
ter
n
a
t
io
n
a
l
J
o
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rn
a
l
o
f
S
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f
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Co
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p
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ti
n
g
,
v
o
l.
8
,
n
o
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5
,
p
p
.
3
4
0
-
3
5
1
,
2
0
1
3
.
[
1
0
]
Ra
v
ik
u
m
a
r.
G
.
K,
e
t
a
l
.
,
―
A
S
u
rv
e
y
o
n
Re
c
e
n
t
T
re
n
d
s,
P
ro
c
e
ss
a
n
d
De
v
e
lo
p
m
e
n
t
in
Da
ta
M
a
sk
in
g
f
o
r
T
e
s
ti
n
g
‖
,
IJCSI
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
S
c
ien
c
e
Iss
u
e
s
, v
o
l.
8
,
no.
2
,
M
a
rc
h
2
0
1
1
.
[
1
1
]
Ra
v
ik
u
m
a
r
G
.
K
,
e
t
a
l
.
,
―
De
sig
n
o
f
Da
ta
M
a
s
k
in
g
A
rc
h
it
e
c
tu
re
a
n
d
A
n
a
l
y
sis
o
f
D
a
ta
M
a
s
k
in
g
T
e
c
h
n
iq
u
e
s
f
o
r
T
e
stin
g
‖
,
IJ
ES
T
1
1
-
03
-
06
-
2
1
7
,
v
o
l.
3
,
n
o
.
6
,
p
p
.
5
1
5
0
-
5
1
5
9
,
Ju
n
e
2
0
1
1
.
[
1
2
]
Un
d
e
rsta
n
d
i
n
g
a
n
d
S
e
lec
ti
n
g
Da
ta
M
a
sk
in
g
S
o
lu
t
io
n
s
-
Cre
a
ti
n
g
S
e
c
u
re
a
n
d
Us
e
f
u
l
Da
ta
-
S
e
c
u
ro
sis,
L
.
L
.
C.
Da
ta
M
a
sk
in
g
:
W
h
a
t
Yo
u
Ne
e
d
to
Kn
o
w
W
h
a
t
Yo
u
Re
a
ll
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Ne
e
d
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o
Kn
o
w
Be
f
o
re
Yo
u
Be
g
in
A
Ne
t
2
0
0
0
L
td
.
W
h
it
e
P
a
p
e
r.
[
1
3
]
A
ll
e
n
Dre
ib
e
lb
is,
Eb
e
rh
a
rd
He
c
h
ler,
Iv
a
n
M
il
m
a
n
,
M
a
rti
n
Ob
e
rh
o
f
e
r,
P
a
u
l
v
a
n
Ru
n
,
Da
n
W
o
lfso
n
,
―
En
terp
rise
M
a
ste
r
Da
ta
M
a
n
a
g
e
m
e
n
t:
A
n
S
OA
A
p
p
ro
a
c
h
to
M
a
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Ra
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Evaluation Warning : The document was created with Spire.PDF for Python.