so I'm not sure if you guys are aware
but we're from a company called chain
alysus and today we're gonna dive into
building trust in the etherion
blockchain and sort of what our company
does in the space sorry bout that
I'll keep us closer okay cool so just a
brief agenda about what's going on so
first we're gonna talk about Genesis and
our services then we're gonna dive into
some of the data challenges we have then
we're actually going to have a long
section analyzing some aetherium data
and walking through one of the
investigations we did and then we're
gonna talk generally about the drive
insights we gained so just a quick
background about myself I'm the
engineering manager for the blockchain
intelligence team at che analysis but
before this I was very interested
academically in Bitcoin so for my
undergraduate thesis I created a set of
tools to visualize the Bitcoin
blockchain this was for Imperial College
and these are some of the pictures from
my thesis and the top left you can see
here these are different views of
Bitcoin transactions over here this one
has one input and two outputs the old
way of changing addresses this one has a
lot of inputs and one output so that
would be someone aggregating a lot of
funds and then this has the reverse
which is one input and many outputs so
someone's disaggregating funds this has
generally changed since this this thesis
was completed in like 2015 so the block
size was not as big an issue back then
batching was not something that wasn't
enforced or regularly used so the
blockchain itself has changed a lot
since then just a bit about myself I
really like network theory and graph
analysis this fun little visualization
in the corner is an algorithm I modified
which some of you might know is called
forest Atlas - and I created multi force
multi gravity force Atlas - to separate
different graph entities and down here
you can see a cluster over time so what
does chain alysus do and what are the
services we provide well really we're
trying to build trust in the
conventional ecosystem so I know some
people here
might want a disjoint financial
ecosystem but our work is about
integrating crypto companies into the
existing ecosystem so we work with three
major parts of that ecosystem we work
with the cryptocurrency businesses
themselves financial institutions as
well as law enforcement and regulators
so we found that in general the space
doesn't grow because every each party
doesn't feel comfortable with what's
being provided so you can see
over-regulation occurred and some in
Japan as well as Korea related to some
of the exchange hacks that happened and
it caused a lot of privacy coins to
actually be delisted so first we can see
coin check had to delist a couple of
their privacy coins then this spread to
South Korea and they started having
stricter regulations and more exchanges
followed suit so our goal is to provide
tools that make regulators feel
comfortable with these currencies and
what they can do with them to some
degree so overall illicit activity
prevents the growth for all ecosystem
participants I'm sure some of you have
mixed opinions about the top half but
I'm sure we can all agree that the
bottom two are bad in general each of
these each of the actors in the
ecosystem has their own risks and needs
and we try to address each of these so
for cryptocurrency based businesses they
struggle to get bank accounts it's hard
to get banking relationships without
having some sort of information on where
your funds come from and what risk is
associated with them for financial
institutions if they expose themselves
too much to risk they harm their
reputations and if there are large
finance institutions they will face
legal repercussions and lastly for law
enforcement regulators if they can't
enforce the laws they make or the
regulations they do to control an
industry they won't be comfortable with
that technology growing so overall our
goal is to work with each of the
participants of this ecosystem and give
them value that lets them feel more
comfortable with cryptocurrencies
in the existing financial system
so some of the data challenges we face
we were a very Bitcoin centric company
before but now we've turned a lot to
aetherium so here is actually Bitcoin on
the left and aetherium on the right they
might look similar to you because their
graphs but they're actually quite
different in the structure underlying so
on the left it actually captures the UT
EXO structure with all UT EXO coins and
on the right is the most more account
based structure that we see on any
account based model additionally smart
contracting provides a new layer of
analysis that's necessary we have to go
beyond the transactions and go into the
execution traces themselves we have to
see what sort of execution happens and
what information we can extract from
that so this is an example of a data
field in etherium and it's this is
pretty old now because they're a lot
more advanced decompilers but you
basically decompile the data field and
to try and figure out what the
parameters that were sent are
transitioning to riskiness insights so
well the tools that we provide for the
participants in the ecosystem is
essentially insights on riskiness and
these vary based on each of the customer
themselves so if you're say in a country
that doesn't consider gambling high-risk
you would have customized alerts that
don't say that a gambling activity is
high risk for you but if you're in the
US for example that would be high risk
so we provide a customizable tool known
as kyt - cryptocurrency businesses so
that they can choose the right risk
alerts to get all right now I'm going to
hand it off to Mako who will walk you
through one of our investigations and
analyzing aetherium data yeah so my name
is Mickel and i work in our research
department inch analysis so I actually
get to do some of the fun stuff in the
company so I'm gonna talk a little bit
about some of the the cool stuff that we
do and the the subtitle is like what
happened to the etherium volume in 2017
so that's sort of the story that I'll be
talking
today and then I asked my mom to send me
a picture for my presentation so this is
me in my early data science days I
actually think I'm playing Counter
Strike on this but just to let you know
like we don't take ourselves too
seriously so if you think any of this is
interesting please come talk to us
afterwards we're quite nice guys so
basically what we do is we start out
with this idea of not knowing anything
on the blockchain so this is one of the
visualizations of addresses interacting
in some blocks and then our goal is
basically to turn this into this so we
have an idea of what belongs together
how do the different entities interact
so basically gain knowledge about who's
interacting with who on the blockchain
so this is sort of the background info
it's like we monitor the blockchain and
try to figure out what's going on and if
there's something we don't understand
that piques our interest and we're like
whoa what's going on here and then this
graph is it's the cumulative volume
moved on the etherium Network and oh I
can point cool so in this period here
initially there was quite a normal
growth and then from this period onwards
it's almost looks like a piecewise
function to me so you have like sort of
a normal growth of volume being moved on
this theorem and then it shifts into
basically a factor of five growth here
so that's sort of interesting to see is
like well we know a lot of things happen
in 2017 a lot of icos
there was a bull run on crypto but it
doesn't really explain why you would
move so much ether on the on the network
so this is sort of the starting point
it's like we see something here or like
we can't really explain it so let's
figure out what's going on so we can
basically if we if we divide these
periods into two blocks so we're
basically looking at the initial period
on the network and then this high-growth
period and one thing we can do is we can
look at the frequency distribution and
what that means is basically we're
taking all the transfers from that
initial blue period down here and saying
okay how much volume was sent in this
period
and which amount are accountable for the
total volume so this graph basically
shows that more total volume was sent on
the network through smaller transfer
amounts so that makes sense if you're
like a general economic Network where
it's like where most people don't have
so much money so they're probably going
to be interacting with smaller amounts
so there's going to be more volume move
through smaller amounts so this is sort
of intuitive at least to me when I look
at it like this looks like normal
behavior so what happens when we overlay
a period of that 2017 period so this is
quite interesting so this to me
definitely doesn't look like normal
economic behavior it almost looked like
artificials that you have these almost
smoothen out regions and then they just
like sort of drop off here so when we
see this one thing is quite noticeable
is that more volume is being moved
through larger amounts on the network so
then again you can hypothesize well why
are some people or entities moving more
funds through larger amounts so then you
can I mean we're basically detective so
having a list of well is it because
someone wants to boost the volume
artificially or are there just more
people transacting in a normal way this
definitely doesn't look normal to me so
we can also look at what type of
addresses were involved in this increase
in volume movement on the network and so
this graph is basically showing that for
for this period like how much volume was
sent through old addresses and what I
mean with old addresses is addresses
that were seen previously on the
blockchain with respect to so if we're
in this bar here for example then old
addresses means that addresses that were
seen before that or if we're over here
it means all all the volume that was
moved by addresses that was seen before
this point so it basically shows how
much volume is being moved by addresses
that were seen before in the blockchain
and then we can also overlay a graph of
how much volume is being moved by
addresses which was only or first seen
in that block range so basically newly
generated addresses for any point on the
timeline
so when we do that that also looks kind
of cool because here we definitely have
a clear pattern is that something is
going on in 2017 like a lot of volume is
being moved and we know it's to larger
amounts and now we also know it's it's
from newly generated addresses so I mean
I could show you a hundred slides and
this is just like a very small peek of
how we would analyze something so we
obviously we have a lot more information
to go in and say what is like what is
going on in the blockchain but when we
piece all of this together then we can
sort of we have an idea of which
addresses so we know they're newly
generated we know they'd move a lot of
funds to large amounts and we know a
bunch of other things also so we can
sort of define which addresses are
involved in this movement and then we
can cluster them together and when we've
done that we can sort of do graph
analysis as well so we sort of look at
how are they interacting are they
interacting in the same way because just
if you're a specific address you can
still be interacting with other
addresses in a different way so that
doesn't really mean you should be
clustered together so this is it's a
long process but when we're done we
basically reach something like this
where this is a visualization of 25
blocks in 2017 and all the orange dots
here are the addresses that were
connected together that's basically
responsible for this huge movement of
funds yeah I just like graphs so I just
wanted to give you a close-up so what
can we say about yeah so another thing
is like we know that the addresses
responsible this belong together so it's
one entity so what can we say about this
entity when we know an entity is
responsible for moving so much east in
2017 well we can go in and say okay we
know all these addresses belong together
let's look at how much volume they send
to each other and here it's denominated
as percentage of the total network
volume so in these block numbers around
60 to 70 percent on average was sent
inside of this entity and then the
orange is basically the funds that is
going outside of this entity so this is
yeah around 2 to 3 percent that's
leaking over time so there's a huge
difference between how much volume is
being moved
and how much volume is being sent
outside of the entity so yeah that's and
I would love to go into detail more
about like how the entity looks and like
this is a project that took several
months so this is just sort of the the
high level details but it's really
interesting when you find something
that's responsible for like for some of
the periods it's over 90% so over 90% of
the volume is actually coming from one
entity in 2017 on the network yeah back
to Syria all right well we're a very
growing company and we're expanding
quite fast so in my team specifically if
you want to work with Mikkel and myself
we're looking for data scientists and a
product manager so if either these
interest you definitely reach out to us
and come chat to us in addition the
company overall is hiring across many
fields and also we have major offices
based around the world and New York DC
Copenhagen London we have quite a few
satellite offices that are smaller I
think coming up around the world so
wherever you're located is probably
likely you can work for us there we're
also very remote friendly so even if you
don't want to be based in an office and
your remote anywhere we also are very
remote friendly my team in particular
half of us are spread around Europe and
then some of us are in New York and
additionally no crypto experience is
necessary so if you want to learn about
the industry if you want to get insights
we would love to train you and get you
up to speed on that and you'll get it on
the job and it's not necessary as a
prerequisite so yeah if you found this
talk interesting please come speak to us
in the sponsors gallery and yeah come
say hi
you