uh our next uh presentation is lightning
Talk number four uh from uh Professor
wihi son uh wihi is in the Comm the CIT
computer information technology cyber
security Professor here at Purdue
University we'll be presenting three
body problem in privacy protection chaos
or New Hope when hi welcome thanks for
the nice introduction uh my name is wi
heisen uh from the Department of CIT um
oh
okay all right um so uh my research uh
revolve around the these four areas um
so privacy enhancing technology
development um and zero trust
decentralized system we design
blockchain applications we uh study the
security and privacy problems there and
we try to improve the performance of
blockchain by option Solutions um we are
also interested in AI security and
accountability uh we uh designed the
proactive defense for the like federat
learning system and also we want to
provide interoperability to the Deep
reinforcement learning agents to
understand the critical actions and the
time stamps
um and uh yeah we're also doing research
on the Cyber physical systems uh we uh
do the you know the the the
vulnerability
Discovery and SW
attestation uh and also the
authentication right so you may find
more information on my personal website
or scan this uh code all
right so what is three body problem
right I I believe most of you you know
uh probably know this because the recent
you know the TV series on Netflix right
uh so it's the so this tell the story of
the AL Invasion we're all fascinated
about right uh and uh this novel I think
is based on the Sci-Fi um that um this
Chinese sci-fi and with the name Santi
in Chinese um I think they are the it is
believe the you know the best sci-fi and
you know the one Hugo World in 2015
right um I'm a big fan of this so it's a
very very fascinating story all right um
so what is three body problem right so
let's have a little bit about the
physics here right uh according to the
Wikipedia all right the the problems is
that given the initial positions and
velocities of three ma uh Point masses
is very challenging to predict uh
predict their you know the their uh
subsequent motions right there's no
General close for solution and the
resulting dynamical system is chaotic
for most initial conditions this also
explains why you know in the novel The
aliens they want to they want to escape
they want to find a new place to live
because in their solar system there are
three
Suns so you can imagine how devastating
it will be right to uh to their survival
right uh so basically there's no
clearcut between day and the night and
you cannot predict the season changes
right so um this is a very very hard uh
problem in physics right um to see why
we have a uh you know similar problem in
privacy protection let's look at the you
know the the the the Privacy uh
preserving technology we're targeting uh
in this case right that's the local
differ privacy so
ldp uh you know in the in the model in
the Assumption of ldp that we on one
hand we have the server the service
providers they're interested in
individual users data but on the other
hand the users they're reluctant to hand
over their data because they may contain
sensitive information right so uh ldp
address this problem by um add noise
quantifiable noise on the local site I'm
not sure I can use the laser no all
right yeah the the user site you add the
noise to the data so your true data
never revealed to the server right and
the server just collect those uh noisy
reports and aggregate them and in the at
aggregate level the the data still be
useful to the downstream applications
right so ldb has been widely used in
many commercial products for example the
IPO loss they are using ldp to learn
information about
emojis um Google Chrome browser they are
also using ldp um to learn the you know
the your browsing history and homepage
information Microsoft impl ldp to learn
how their customers interacted with
their products
right so you know B on that you know
according to that description of ldp we
can see that there's a clear tradeoff
between privacy and utility right more
noise added means that you know the
degraded the quality of the utility
right uh in general we can tweak that
balance uh with this parameter abson we
call the Privacy budget right and by
tweaking that we can accommodate I think
many you know uh the the requirements of
the privacy and utility needs for many
applications right um so this is the map
on the right hand side that's the map
that shows the population density of the
Indiana State that's the reflect the
true uh ground truth information and the
on the left that's the after the L DP
after the you know the noise injection
uh so this is the uh the utility we can
get right yes this might not precisely
reflect the real word but still to some
extent still we can see the rank of
those uh population density by their you
know by the counties and we can still
use it right so we can tweak that
parameter do this this is
nice all right so let's revisit the thre
model right we we said that in this is
the original Assumption of the ldp that
would believe the users they're
vulnerable to the uh the Privacy linkage
so we want to protect them right and we
consider the server they might not be
trustful you know uh they want to learn
the user's privacy so that's why we
designed ldp to protect the privacy of
the user right this is nice and also we
believe that the user they're compliant
right they they will you know uh
honestly Faithfully execute the protocol
right that's
good but there might be you know another
reality right in this reality that we
consider the users the Thousand millions
of the users they might not have the you
know enough resources to protect
themselves right in that case they may
be controlled they may be comp
compromised by the attacker right so the
goal of the attacker here is that they
want to manipulate the result manipulate
the
utility all right so and in this case we
can see that the server becomes the
victim becomes the victim of the data
manipulation all right we have to deal
with this coexistence you know the these
two different uh threat models in
practice
so this is a disappointing attack we I I
believe most of you already you know
know this attack is attack itself is not
new it has been you know uh discovered
in many decentralized systems for
example The feder Learning System right
we have the uh disappointing attack but
the difference here for the
ldp uh we're not you know directly
inject the data to the ldp instance that
the
randomizer we found that you can bypass
that we allow attacker to craft the data
in the output domain of the ldp
randomizer so it turned out this is more
effective than the direct you know input
injection right so this is the we
believe this is the kind of a techn that
leverage the characteristics of ldp and
achieve the more you know uh effective
result in the
end all right so and then the result
will be
controlled right um ldp uh data person
attack is not new but you know recently
discovered
independently uh in the 21 SB paper they
come up yeah they come up with this uh
attack they call the manipul
manipulation attack where is the kind of
a uh data person attack their goal is to
degrade overall performance of the ldp
all right I don't care you know uh you
know prise control I I just you know I I
just don't want to mess up right and uh
the same year uh in the US 21 paper um
they consider scenario like uh the
recommendation system right they want to
promote unpopular
items and to elevate them and to the to
the front right in the higher ranking so
this is their
scenario and and last year our using
paper further uh push the boundary we
found that ATT attack actually can you
know precisely control the result to
some intended value so we call the fine
grain manipulation fine grained data uh
disappointing
attack right so whatever you know case
you're in uh obviously the the service
provider is the victim Ser service
provider may you know suffered from you
know the uh economic loss and in this
case um but considering the potential of
this technology adopted in many internet
applications right it may have a you
know broader impacted you know beyond
that for example it's going to you know
affect the trust or our confidence in
these service providers right and
another thing is that uh you may you
know consider that this may also harm
the internet
Freedom right so consider a situation
that you know a repressive government
they want to control the information
that's the censorship right control
information on the
internet and uh they don't they don't
need to directly to Target any
individual users but instead they can do
disappointing attack so their goal is
that they want to change the final
result right can say there this is some
uh online election for example right
they can do that they can do this
stealthy censorship and
suppression
right right
um eventually we we we we we land on
these problems three body problem in
ldp right we have privacy we have
utility and now we have a third B Factor
uh
security so privacy and the utility
their relationship is kind of well known
their trade-off but we try to understand
the other two sides right uh privacy
security and utility and security so
what do we can or what do we know so far
right
um for the security and the Privacy
privacy research reveal that uh first so
we have this tradeoff we also have this
trade-off relationship say you have
higher
privacy which you know makes your prodal
more vulnerable to attack so lower
security but in our paper we also show
this consistency
relationship higher privacy actually you
know Elevate the
security so you may want to know which
one is
correct right because if have a clear
answer we can have we we can design more
effective
solution
right but unfortunately both the answer
are correct it really really depends on
the attack
goals right and also the strategies
you're
using so there are different conting
attacks and something that we cannot
predict in advance we cannot predict
that so we cannot adopt the the more
effective way to defend against
it I'm sorry all right so recently we
just published the paper about this um
on archive so we want to you know
emphasize this the this robustness angle
this something that additional to the
you know utility measurement this should
be a another very important factor when
you design ldp protocal we also review
the new relationship between ldp design
and efficacy uh attack performance and
we also you know propose the zero shot
detection right without it means that
the the the uh service provider don't
need to know the ground TR information
right so they can they can
detect for the security and
utility um it generally believe that the
utility has been destroyed in this case
because attacker wants to control right
um and then most the work there
you know working on you know to kind of
recover the result but their goal is to
reduce the attack again but reducing
attack again doesn't really you know
mean that you can uh bring up the the
the utility because here we're using
different matrics to match of these two
uh perspective aspects so there's a
mismatch all right and also must the
work assume that there's a attack is go
uh is ongoing
so uh currently we're working on review
the inherent relationship and also we
want to you know retain the utility
regardless if there's ongoing attack or
not all right the limitation of the
current research is that somehow we want
to
reduce that complex comple a complex
unit relationship to a a bilateral you
know simplified model this two body
problem
right that's give us that's good right
we have once we have a simplified model
we make uh have a you know more clear
more you know uh the effective solution
but the problem is that we may get
biased we cannot see the whole picture
you know of the
landscape right but on the other hand
you know to deal with this complexity
there's three factors of uh is is
challenging we're still uh have no
clues so we're looking for a new hope in
this case right
uh yeah
AI yes so but there's a reason right so
over is see that this all those standing
result you know of the llm right you
know understanding the complex real
world
complexity and also they can they know
how to solve these interactable problems
right that's nice right that's also
inspired our our work right in this C
project we're aiming to uh design
machine learnable privacy enhancing
framework our ultimated goal is to uh
you know to inspire or inform the uh AI
assisted privacy technology development
and we want to enable a a machine
intelligence that can understand uh
attend to our privacy security and
utility needs whenever we want them
right uh there are some goals and where
work on them and already have some um
you know promising um you know the the
evidence and eventually we want to build
a privacy
agent machine intelligence that can you
know harmonize the security privacy
utility and they can also provide the
inability uh to their critical actions
so people understand what happening uh
there and uh and also we want to
at the same time we want to provide the
high quality the services you know to
all the users and uh yeah so I think
that's everything here so I I'll stop
here right thank you so much