to speak uh at this year's defcon
voting village i'm going to talk about
how to weaponize rlas to discredit an
election
my name is carson sherman i'm actually
working in copenhagen
at the center for information security
and trust and some of the results i'm
going to talk about today
actually based on joint work with asmita
delela
and oksana kuduk so
let's just dive right in a risk living
mallet you probably have heard already
in the past talks that uh you know
resuming audits what they are and so on
let me just kind of
recapitulate and summarize what they are
so resuming audits are a statistically
sound auditing method so there's no
question about it
and you know i think it's really
important to emphasize
that it is a sound method it's
statistically
proven correct many people in the world
have
looked at the mass behind it they all
agree that this the mass
the mass of testing is right and
so there is in this talk i am not going
to
put in question if resuming audits are
sound or not
i'm just saying um they are sound
and the idea of the wristling audit is
of course to identify
and automatically correct an erroneous
election result
and i have here actually made a little
drawing of
how wristling audits actually work um
you basically have paper ballots there's
a paper
ballot manifest which tells you how
the um ballots are stored where you can
find paper ballots and so on and then
there
is of course the result
here's my laser pointer so here's the
result and when you start a risk
limiting audit
you first need to kind of compute the
sample size and the way how you do it is
you take the results you know depending
on what kind of a system you have
you look for the margins um you have to
determine what kind of list
risk limit you want that means how
confident do you want to be that
a erroneous election result was actually
being caught and corrected
and once you have that you have computed
a sample size now you have to draw
a random sample of the ballots and for
that you need the ballot manifest
and you need to enter some kind of
entropy to make it
truly random because a restaurant audit
that doesn't draw a random sample is
actually not a very good
audit so once you know which ballots you
want to draw
you actually then can start going to the
ballot box and do the real audit you
basically follow the information um of
the sample which tells you you know go
to this
thing and check out this ballot and do
whatever you have to do with it
and then once you have done the audit
and the outcome is like yes
i found enough correct uh statistical
evidence to kind of support
the claim that the election result is
correct or i i didn't
in which case i have to go back go to a
bigger sample in the first case
we'll have to do a full uh recount so
this is the idea of a risk living audits
and we all know that risk limiting
orders come in many many different
flavors
flavors there's the pop a belt pulling
audit which is in order
where we are going to draw a sample and
trying to kind of
find support statistical support for the
correctness of this election result
or we do a ballot level comparison audit
where we for example check
that the ballot is actually correctly
registered on the
cast vote record the cvr record so there
are many different ways
and the reason why i'm actually saying
this is because
in this talk i'm actually going to focus
mostly on
ballot level comparison audits because
there was the easiest and that also
scales up to other countries beyond the
united states because
i believe that the findings are actually
kind of important not just for the
united states but also for other
countries
so now if we do a ballot level
comparison audit let's just look
at one example here is the result
of the colorado 2016 election
and i'm displaying you show your picture
here of philip stark's tools that
actually
uh computes the uh the the size
of the sample and so how does it
actually go about doing this
yes so you um basically take that tool
which is available from philip
stark's homepage you type in the numbers
and then at the end
you um just press return you also kind
of
choose the risk limit which is here 95
percent but the risk is 5
and so you get 95 confidence that the
election result is correct and
you do something with these overvotes uh
overstatements and understatements
um uh no worry about that but once you
hit calculate size
it actually kind of comes up here as a
result and the result is 142 ballots
okay so i have given this talk and
showed the slide many times in the past
and what my experience is
that people especially election
officials they're really excited about
the audit
data risk living audit but when they
hear the number 142
they say like oh yeah how can that be
true this cannot possibly be true
that's just not uh that's just not that
doesn't sound
right only 142 votes you have to
look at in order to be 95 sure that the
the election result of the presidential
election 2016 the collateral result
is actually correct um
yeah so because i've given this talk so
much
and i've seen the faces of the audience
it got me thinking
it's like you know with receiving audits
what we want to do is we want to make
sure we want to provide
some form of evidence and check that the
election result is correct
not necessarily recounting the whole
thing but just do it in a statistically
relevant way okay
but if the number is so small can that
be
misused in one way or the other and
therefore
i actually came up with this this
research question just like
can one weaponize rlas to discredit an
election
and it has always puzzled me that um
the face of the people in the audience
when i talk about this and
and so what i want to show you in this
talk is
i want to show you that i believe that
this is actually possible
and in order to convince you about this
i
structured this talk in five different
easy to follow steps i hope
the first one is um i'm going to talk
about the disinformation
playbook and that's really a
study of a group of
scientists concerned scientists they
came up this uh
you know different place how companies
actually in the health sector
are um you know using or misusing
information to construct disinformation
campaigns that are actually helping
their cause then second step i'm going
to identify vulnerable assumptions in
the
rla and one is of particular interest
and that is that of the sample size then
we're going to conduct or describe to
you a
you the results of a user study that we
have recently conducted in the united
states
and then i try to argue that with that
information at hand you actually can
design a disinformation campaign
where people when they listen to it and
are easily made to believe things that
are
not right one can kind of change their
one most likely i should say can change
their perception
of that and lastly i would like to talk
about
a few defense mechanisms that i think
are mostly informal and some
are more useful than others but i think
there's some
more research to be done okay so let's
look at the
disinformation table so this information
playbook as i already mentioned
um is was published in 2017 by
the union of concerned scientists now is
that a good reference well i don't know
but
it actually kind of when i read these
five points um
it made a lot of sense to me i'm not
sure if these are the
the that's a complete list of things
that one can do um
in order to define or if it classifies
all possible ways to organize a
disinformation campaign but i think they
are definitely
uh valid and useful so let me just kind
of review them
there's the fake play of that is that's
what that's always this
in front of it but you do counterfeit
science and try to pass off legit
legitimate research so you know here for
the rlas
it could be uh you know
making some other statistics which is
obviously wrong and say like you know
see this is how you should do and not
like the ones
how the relays are really conducted the
blitzes you harass the scientists to
speak out with the results and
inconvenient views um i think the
community around all this has become so
big
that this is actually not really a
concern but nevertheless it's one of the
place
in the disinformation playbook the
diversion technique
is your manufacturer uncertainty about
science where it is possible
so i think the diversion is actually
where you leave the science and the
results
intact but you manufacture certain kind
of arguments and
collect evidence that can show
where you misuse the signs and you argue
exactly the con
contrary so that this diversion play is
the one that
will keep us busy and we'll return to it
at the end of this talk
then there's the screenplay you buy
credibility so alliances with academic
and
professional societies to make it look
okay
so i cannot talk about anyone else but
you know i've never been approached
on any of those issues and then there's
the fix you manipulate government
officials or processes to influence
policy
um that kind of is also somewhat
relevant
here but we will see it's not the main
focus of this talk but the main focus is
the diversion
so that's the disinformation playbook
when i say like
um you know you do it i'm talking about
some kind of an disinformation architect
an adversary who is trying to kind of
influence public opinion in a particular
way and try to sway it either
this direction or the other so the
question therefore is
who are the disinformation architects
that might have an interest
in order to do something about fiscal
limiting audits
and i think they are there we have to
distinguish two levels namely the global
level and which
are nation states who are interested in
meddling with one's election
and there's of course the local level
which actually is
not to be neglected where there are
activists and political parties
that try to argue and uh and uh um
argue their way want to uh you know
discredit and
risk them ignored it in one way or the
other okay
so uh you know the local level i think
is like
when we talk about election security in
the general
space i think the local level is
particularly worrisome because
it actually takes only very very few
people to influence
public opinion and here we have a um
on the right you see a newspaper
clipping a majority of coveted
misinformations comes from only
12 people so that's a very recent
that's a very recent observation
maybe one or two weeks ago okay so this
is what i wanted to say about the
disinformation play
playbook let's move on and talk about
identifiable vulnerable assumptions in
risk limiting audits
and i would like to kind of point out
three okay
so there's the first assumption that we
make that is that the integrity and the
security of the paper trail
is intact for noted so that means that
after
ballots are cast on election day and
before the audit
is commenced it's usually you know a few
days a few weeks that pass in the middle
now in between we need to kind of make
sure that the paper trail is secure
okay and so we assume that the security
mechanisms
that are put in place actually are
sufficient
another part of this assumption is also
you know you also have to
to kind of assume that the risk limit of
95
is acceptable for the people i mean if
it's too too little
um if they say like well five percent uh
risk it's it's not unacceptable for us
we need to be 99.9
sure sure um you can do it but when you
do an audit you have to deter
define the risk limit before you start
the audit
assumption number two yeah it's the the
sample size
that you actually compute so the number
that comes out of those things as i
mentioned earlier can be very very small
142 for a
ballot level comparison audit in
colorado um
not a very big size and so the question
is like do people believe it
do they accept it or could you maybe
influence them to kind of uh think about
this in another way
and the third assumption is at the end
is that the sample is actually drawn at
random and not
you know staged and said like you know
here look i pick my 142
ballots they support everything but you
have already pre-computed exactly
and sorted them in such a way that the
142 ballots
are actually exactly there to support
your your claim so
this is why the statistical sample has
to be statistics now for this
talk i'm only concentrating on if the
number of ballots to be sampled is
trustworthy
okay so that's uh that's the the thing
that we want to study
and uh for that we actually made a user
study
so we made a user study and we went out
and we used a particular
platform the prolific platform which is
used by many social scientists for doing
studies like this
and we just asked people okay so
what what do you think about risk
feminine audits
uh would you accept that result and so
on and below here
you see an example so uh so number one
is
the uh participants that we chose
they're all u.s citizens and the
prolific platform it's a crowdsourced
platform
you can just kind of click you know
where do you want the participants to be
from
and because we made the study right in
the aftermath of the
2020 election we thought like oh that's
that seems to be a good
thing so let's just ask those people who
have maybe read something about resuming
audits
in the newspaper especially in georgia
but we just kind of picked
any kind of demographic um among all of
the united states citizens that are
registered on the prolific platform and
then our
um our
user questionnaire looked a little bit
like this
we basically made a fake election which
has about three million votes cost
okay and then we also for every
participant
we changed and and you know modified
slightly
the margin between the two currently
that so it's a it's always an election
with two candidates there's candidate a
and candidate
b and uh you know here
is um okay here
and then you you the um the
participant has to answer to certain
questions so
one of these questions would be if the
candidates support loss in the election
in the initial so do you think an audit
would be a good option to reconfirm the
election result
and yes no maybe and so on so we kind of
wrote out um the we kind of introduced
the risk limiting audits just with a few
words the beginning of the
study so if the participant read this
stuff carefully that we presented
should understand uh basically what is a
risk limit audit
but at the same level of detail as i've
tried to explain it to you here in this
talk
okay so now what were our findings
so the first one is i call the law of
the untrusted small numbers
and so we had actually two hypotheses
that we wanted to confirm or
yeah that we plan to confirm and both
were actually confirmed and i'll show
you the
some graphs on the next slide so here's
uh hypothesis 1.1
when asked about their opinions about
which number of ballots should be
selected for ordering the participants
provide a number higher
than the one prescribed by the rla
methodology
and you know here the methodology is as
i said a ballot
level comparison audit the hypothesis
1.2
is that the participant's confidence in
the order results changes when they are
actually informed about the number of
ballots selected for audi
auditing and both of those things
are are what we could confirm and let me
show you
the graphs okay so here are two graphs
the first graph basically here on the
left
it shows you it's a logarithmic scale as
you can see
between 0.01 all the way to ten percent
this would be a hundred percent on the
very top okay so the
top line is like all of the people who
wanted to kind of have a full recount
anyway and here at the very bottom
zero these are all of the people who uh
didn't want to have a uh you know didn't
want to have a recount
at all or wrist them in an audit at all
and here these red points so these
orange looking points
these are basically for each one of the
questionnaire we generated a different
number
and you can see they were uniformly
distributed which in retrospect wasn't
that a clever idea because
here they're very very few uh
[Music]
very very few dots in the middle because
when it goes to uh you know
it's just because the thing gets so
incredibly um
you know it grows very very quickly
so yes you can see here
almost everyone wanted to have a number
felt comfortable with a number
uh above and only a very few set of
people a group of people actually wanted
less
um so we could confirm you know doing
all the statistics and analysis and the
testing and so on
we could confirm the hypothesis number
one
indeed that people wanted to have a
higher number
but this is not the interesting part i
think the interesting part is really
the second graph here that you see on
the line are people changing their minds
when they actually learn
about what is the number chosen okay
and so here you see these two axes
there's the before axis and these the
after axis
so before many people were actually kind
of happy and okay with that
but if you tell them what the number is
suddenly people were not definitely yes
was off the table
they were very surprised that these
numbers were so small and they didn't
think that this actually kind of it
didn't kind of grow any confidence
in our sample and again we
we did the statistics we did the
analysis and we could confirm the
hypothesis
okay so uh this was uh you know this was
the uh the first
you know the first two hypothesis or the
first hypothesis two sub-hypothesis
and we also had second hypothesis the
second hypothesis is
how about the justification for the
sample size okay
so is that you know the the sample size
comes out of a tool
and the question is is there a
difference in the effects on the voter's
confidence in audits depending on which
selection criteria backs up the chosen
number of the audited balance
and so what we've actually thought we
kind of asked you know
hear different uh um different
basically sources yes so there's the
peer-reviewed scientific paper that
tells you how big the sources
and independent experts so
not the experts that were um tried to be
bought in the disinformation and cook
so but really independent honest to god
you know honest people
an agreement among political parties our
political parties
do they agree and uh so that we have
mandated by court the court says
you know so and so many ballots have to
be audited
non-governmental organizations could
also um enter the ngos could also enter
the picture and to give
give some some suggestions of uh how big
the numbers on just general legislation
so
what we actually found out is yes
so uh so here the peer review paper
most of the participants really trust
the academic
the academic performance and
very many many fewer
actually the legislation so again we did
some statistic analysis
and we could confirm um this this
hypothesis
okay so now we move on to um
the limitations of the user study okay
so this was a quantitative research
study
of course one could have done a
qualitative research study as well which
being part did but i'm leaving this out
out of this
out of this presentation and there is a
paper
where we describe all of this which
shortly
appears on archive x and has been also
accepted in you know at the e-vote id
conference
in bregenz uh in austria
this year so what else is there there we
have to kind of make the the standard
bias disclaimers okay
although many social scientists are
using crowdsourcing platforms
uh like it's like prolific to do their
studies
there's an inherent bias that might come
with that namely that only people with
internet access actually kind of
participate in this
more wealthy mobile educated people and
that you know the same kind of comment
and concern
that you might have about the other
studies also i see no question about it
so the third point is the limitations we
have thought about
ballot polling audits and we have not
looked so much into ballot polling
audits
but more in comparison audits and the
reason for that is
because the study was actually meant to
be more global
we did not just focus on the united
states but we've
also wanted to kind of look at other
countries for example kenya
sub-saharan continent um you know can
you actually use
wrestling audits in order to instill
trust
more trust in these kind of countries
so um so this is why we have not done
actually
um and our questions did not aim to kind
of
validate the choices that the valid
polling audit would give
but only the comparison audits so we
also did not
ask for any prior questions regarding
prior knowledge on rlas
we just kind of asked
the people you know here listen to this
these are the numbers do you trust it or
not
yes okay so moving on
to how do you design a disinformation
campaign out of us
okay and uh we only do this with the
law of the untrusted number so the first
thing i would like to kind of point out
here
is that uh there is actually
no disinform or i could not identify any
in
this information using my humble google
searching
skills okay so i just looked for
um on google as like georgia
you know which actually used the risk of
being ordered in the 2020
election said like you know what about
georgia georgia risk the main audit
and so i went to this this list of
the list of papers here i've been to fox
news i went to cnn i
tried to cover all of the different
media and all of the media reported on
the wrestling audit
but they all did it in actually very
professional neutral and technical way
so it was almost like you know there was
the only opinion i could actually
uh see in some of these articles were
like oh always
relays are good but you know it's
essentially they're just recons recons
are always better
our laser i'm just you do an rla
if you want to uh you know speed up and
save costs
and don't have to look to add so many
ballots so that's the only opinion so
it does not appear to me that anyone
actually tried to kind of make a
disinformation campaign
out of this uh law of small
numbers that we we identified to bizarre
with our user study okay so now
you have probably already guessed it the
diversion disinformation plan would be
very very simple
you're just making a subs an
unsubstantiated claim that the sample
size is too small
and many people will say like oh my
goodness yes the sample size is too
small
and that would be the cause of
you know people actually trust that you
know changing their mind about
should you trust the election result or
should you not trust the election result
and then once you make that claim now
you have to kind of find a sample a
a way to kind of distribute that
information across
the network and you can either go to
your favorite news outlet or you can do
social media or you can do whatever the
12 people did or 17 people
who spread all of the disinformation
about corporate vaccines
it's not that i don't think it's
terribly difficult i think it can be
done and i think it's actually uh
uh quite problematic and if something
like this might happen and it's it's i
think it's
it's it could be you know maybe one of
the conclusions of the talk is
um shall we just uh you know maybe think
about
um first of all what you know
what can we do in order to mitigate
the the impact of such a
diversion disinformation play
and i think but we come to to these
mitigations
in a little while okay
so the analysis that we've conducted
very preliminary here
is that such a disinformation
campaign would probably work best when
the margins
are wide because margins are wide
sample sizes are low sample sizes are
low
people don't trust it and you have a
problem so that's actually
an observation which is which is uh
somewhat comforting
also when the when the um sample size is
too small it will
the our label trigger a full recount you
know then
it's also um a uh
a technique or you know it's it will
strengthen
if it's a full hand recount there's no
it's basically a recount it's no longer
an
rla and it will probably not affect uh
the trust
the things you know so the uh um
the smaller the margins um or the larger
the margins
um the more the higher the risk
that uh such a disinformation uh play
would actually be
um successful so
um yeah i just want to mention
studies related to butter polling audits
can be left to future work
so for us it's actually no problem to
generate different
questionnaires that we can kind of send
out to people using different
crowdsourcing platforms
which we plan to do in the fall we
haven't actually gotten to it yet
but this is left to future work
and there's a disclaimer so you know
just these and maybe i'm repeating this
one more time too many
but uh we are talking about comparison
audits here and not ballot polling audit
so when you do a ballot polling audit
then usually the numbers of the samples
are
slightly bigger all right
so we're moving on to the defense
mechanisms and the defense mechanisms is
like
what can you actually do against a
threat like such a diversion
disinformation play okay so i think
there are if there are four things that
we have identified
and the first one would be you know
target smaller constituencies so when
you do a risky ring audit
don't just do it in an entire country
but kind of
find a jurisdiction level constituency
level that is small enough
so that the the problem can be better
compartmentalized
now of course this idea is very much
against any kind of legislation the
legislation says you have to sample like
this you have to sample like this
but this is it doesn't say that these
these ideas here of mitigations are
practical
or possible or possibly even useful but
these are just ideas
okay the second idea is something that
one could consider i don't know what the
negative side effect is and that is to
artificially inflate the sample size
so the system says you know pick 142
ballots but you decide to
audit 1420 ballots instead
just because you know that you know
people might be
more easily swayed to disbelieve in the
election result
just because the sample size is too
small
you could also strengthen basic
education in statistics which is like a
very sweeping
mitigation which also is not really
implementable
and it will take many many years to to
to kind of come to fruition but i think
the fact is that
statistics can be somewhat
counterintuitive at times
and we've seen this with these small
sample sizes here so
one way to strengthen um
you know or to mitigate the threat of a
divergent
disinformation play as i've described
earlier
is to just kind of strengthen basic
education and statistics and i don't
know how to do this
maybe uh doing you know using proactive
kind of
advertisement or putting stuff on
youtube explaining the things better
making more palatable making
uh in civil education classes
for kids you know start thinking about
those kind of
techniques of to of how to mitigate
um these kind of threats so these are
all ideas how practical they are i don't
know
and the other one is just be proactive
um that could be
a uh i'm a strong believer in
transparency
so so i think that would be
being proactive and talking about it and
presenting um
the the the real rationales why those
numbers are so small
continue actually um putting these kind
of
information and you know out in the
public discourse that would be one way
to mitigate the threat and with this i
would like
to thank you for your attention and i'm
i'm ready to take
questions thank you so much bye-bye