Homomorphic
encryption
is
a
technique
that
allows
computations
with
encrypted
data
without
revealing
the
or
requiring
decryption.
This
has
many
potential
applications
in
artificial
intelligence
(AI),
where
privacy
and
security
are
critical.
However,
implementing
homomorphic
for
AI
also
presents
challenges
opportunities
from
software
engineering
perspective.
In
this
paper,
we
provide
comprehensive
overview
of
current
state
art
AI-based
discuss
some
important
aspects
solutions
scenarios.
We
review
recent
research
applying
to
their
aspects.
compare
different
algorithms
libraries
terms
security,
performance,
usability,
interoperability,
functionality,
scalability.
Finally,
highlight
open
future
directions
development
area.
Journal of Network and Computer Applications,
Journal Year:
2024,
Volume and Issue:
231, P. 103989 - 103989
Published: Aug. 2, 2024
The
metaverse
is
a
nascent
concept
that
envisions
virtual
universe,
collaborative
space
where
individuals
can
interact,
create,
and
participate
in
wide
range
of
activities.
Privacy
the
critical
concern
as
evolves
immersive
experiences
become
more
prevalent.
privacy
problem
refers
to
challenges
concerns
surrounding
personal
information
data
within
Virtual
Reality
(VR)
environments
shared
VR
becomes
accessible.
Metaverse
will
harness
advancements
from
various
technologies
such
Artificial
Intelligence
(AI),
Extended
(XR)
Mixed
(MR)
provide
personalized
services
its
users.
Moreover,
enable
experiences,
relies
on
collection
fine-grained
user
leads
issues.
Therefore,
before
potential
be
fully
realized,
related
must
addressed.
This
includes
safeguarding
users'
control
over
their
data,
ensuring
security
information,
protecting
in-world
actions
interactions
unauthorized
sharing.
In
this
paper,
we
explore
future
metaverses
are
expected
face,
given
reliance
AI
for
tracking
users,
creating
XR
MR
facilitating
interactions.
thoroughly
analyze
technical
solutions
differential
privacy,
Homomorphic
Encryption,
Federated
Learning
discuss
sociotechnical
issues
regarding
privacy.
Through
the
development
of
metaverse
concept
from
Sumerian
myth
(5500
-
1800
BC)
and
mind-altering
novel,
“Snow
Crash”
in
1992,
to
today’s
information
age,
human-
society-centred
urban
worlds,
an
extension
residents
society
where
virtual
physically
real
blend
are
more
organically
integrated,
meant
mirror
fabric
life
with
no
harm
their
residents.
The
success
cybercommunities
depends
on
quality
data-driven
Smart
City
(SC)
Digital
Twins
(DTs),
seamless
exchange
data
between
cyber
physical
worlds
(e.g.
counterpart
“Avatars’’)
processing
effectively
efficiently
vicious
interventions.
potential
risks
this
ecosystem
that
incorporates
Web3
can
be
extremer
than
ones
Web2
since
users
immersed
multiple
tightly
coupled
wearable
sensor-rich
devices
perceiving
possible
imminent
negative
experiences.
This
study,
by
analysing
cyberthreats
cyberspaces,
proposes
a
blockchain-based
Decentralised
Privacy-Preserving
Machine
Learning
(DPPML)
authentication
verification
technique,
which
uses
immersive
instrumented
against
identity
impersonation
theft
credentials,
identity,
or
avatars.
International Journal of Safety and Security Engineering,
Journal Year:
2024,
Volume and Issue:
14(1), P. 125 - 133
Published: Feb. 29, 2024
Social
networks
have
become
integral
to
our
daily
lives,
facilitating
connections,
information
sharing,
and
community
engagement.However,
concerns
regarding
privacy
security
emerged
with
their
widespread
use.This
paper
delves
into
specific
risks
associated
social
media
use,
including
data
breaches,
identity
theft,
cyberstalking.The
analysis
extends
various
measures,
such
as
encryption
protocols,
two-factor
authentication,
advanced
browsing
techniques
enhance
user
protection.In
study,
78%
of
users
reported
experiencing
issues,
shedding
light
on
the
prevalence
nature
challenges
individuals
face
platforms.These
issues
encompassed
cyberstalking,
underscoring
urgency
addressing
these
concerns.Moreover,
research
explores
strategic
approaches
for
mitigate
challenges.This
involves
implementing
stringent
protection
policies,
increasing
transparency
usage,
empowering
exert
greater
control
over
personal
information.Beyond
academic
inquiry,
practical
implications
are
significant,
they
directly
impact
well-being
users.This
provides
a
comprehensive
overview
current
landscape
emphasizes
importance
proactive
measures
safeguarding
networks.
Private
Information
Retrieval
(PIR)
is
a
two
player
protocol
where
the
client,
given
some
query
x
ε
[N],
interacts
with
server,
which
holds
N-bit
string
DB,
in
order
to
privately
retrieve
DB[x].
In
this
work,
we
focus
on
single-server
client-preprocessing
model,
initially
proposed
by
Corrigan-Gibbs
and
Kogan
(EUROCRYPT
2020),
client
server
first
run
joint
preprocessing
algorithm,
after
can
elements
from
DB
time
sublinear
N.
Most
known
constructions
of
PIR
follow
one
paradigms:
They
feature
either
(1)
linear-bandwidth
offline
phase
downloads
whole
database
or
(2)
sublinear-bandwidth
however
has
compute
large-depth
(Ωλ(N))
circuit
under
fully-homomorphic
encryption
(FHE)
execute
phase.
Artificial
Intelligence
and
Machine
Learning
are
widely
integrated
into
real-world
applications,
facing
security
privacy
risks.
The
emergence
of
quantum
computers
poses
a
substantial
threat
to
ML's
long-term
security.
Our
study
delves
the
intersection
ML
with
in
post-quantum
era,
where
Post-Quantum
Cryptography
meets
ML/AI.
