So here we are at section 5.3. In
this section. We will be covering securing data in privacy. This
section is going to take a completely different
turn and not talk
about the the technical things so
much, but definitely the digital
rights and the privacy issues surrounding
these Solutions.
So when opening Urban datasets to the public is
proven to be awesome and possible as
described in the last section. We're also creating
threats that relate to the privacy
and and digital rights.
So in the last section, I painted a very Rosy picture
about the beautiful opportunities that are created when you
open the data and share the data, the
road to hell, though is page with good intentions. And
so, I want to, in this section, take a little
bit of a turn and talk about the importance of securing public
data, IE privacy and data
protection, 's. So what you're seeing here
is from the m stands. Archive is the an
example of the hollandsworth punch card.
Which was developed to help municipalities tackle,
this kind of late, this kind
of redundancy of collecting citizens informations and
tabulation tasks. And so by 1939, the
Dutch government made it mandatory for municipal municipalities
and governors to
maintain a personal ID and that recorded a
lots of different kinds of sensitive information, like gender
Age, religion, political party Heritage,
and even ethnic
Ins. And so, on one hand, this will solving lots
of problems for the government and then local municipality. On
the other hand. It became a huge problem. Once the Nazis took over the
government's later on during World War 2.
Big game hunting is a popular term
used in the cybersecurity industry. Describing
the number of popular
attacks by hackers and ransomware
people and the
was interesting here, is that in
this diagram? You see that governments and municipalities are
still a very, very popular Target for
these hackers. And
the first diagram shows 45% of
the u.s. Municipalities attack by ransomware had
a population of 50,000 and it doesn't 19. So
just a couple of years ago. These were very, very popular
targets, not big, big cities, but even the smaller cities and
seven, there were 70 US state local governments
that had been attacked by ransomware in
2019, and they were these
ransomware attackers were able to raise
about.
7.5 billion dollars that
were in fact, paid out by the state and
local governments. And so these are
scary statistics about cities
being a popular Target because they're not
familiar with the cybersecurity rules
or threats and they're not hardening their
systems. So as they are doing
a great job, opening up data, we
have to be very very careful about and how they do that.
And
Kinds of data can be exposed if and
when breached.
So once the data is stolen, there's different kinds
of hierarchies of What kinds of personal
data you get stolen. And so here,
specifically what they have on the
the bottom is simple stuff.
Anonymous stuff, like environmental, data, biodiversity, whether
it's kind of harmless, but then he's, as
you start moving up, you can start looking at
energy data or taxi rides per year. Not
a threat, really.
We but then you move along another step further and
cars can be detected
with license plates with
machine learning natural,
language processing and then the next level
is IP identification or so IP addresses, license plates, Geo
locations and indirect identifiers
identifiers, like addresses biometric
data, like facial mapping
and names Etc.
And these are the kinds of data sets on the top here of the of the
pyramid that you certainly don't. You want to protect at all
costs from from being exposed.
So there is horriffic
kind of e-crime ecosystem where
attacking municipalities becomes
a kind of business model. And I'm
not going to spend too much time on this, but there is the
business model is comprised of service distribution and
monetization. The service part
is made up of a whole Litany
of different characters like access Brokers hardware for sale
ransomware.
Malware packing Services, web
injection kits. And these are the kinds of
systems that they use to exploit the
system. Once once, in the system. There's
there's a distribution layer, which
is using social media. There's actual
kit to do it as spam purchasing
traffic and Distribution Systems, and
then they can monetize
the entire.
System by fraud
or mules or laundering cryptos,
the most popular right now, as you might have heard in
recent media and wire fraud
and dumb shops Etc. And so this is a simple
diagram, but it does paint a pretty
bad picture about what these opportunities and
benefits are for exploiting municipal
datas.
So, as we discussed, there
are a lot of data sets that are being collected. But
what happens if the data is stolen, what,
what's in the data, and
what do the hackers do with it? And furthermore, we
have to be very, very careful about how we choose the
types of sensors and
iot as in the previous section. They're collecting those
those data to make sure that we're collecting
it carefully and very, very mindful.
Privacy.
In this slide, we're looking at a whole catalog of
different data. Collecting, sensors ID
sensors, you know, smart, smart,
technology sensors. These are technologies
that were using here in the city of Amsterdam
as specifically in public
space. So the choices of the sensors that we make is
really important to make sure that we make
sure that the, the Integrity of
the citizens are first and foremost, so,
Your vision for those that you may or
may not know is an algorithm. This train is
specifically classified, different objects, like
bicycles and people and dogs and trucks
and things like that, but it can also detect biometric
data and that's very, very risky.
In my opinion. There's
sound sensors. So these sound pun intended.
Sound collection devices that can detect various
classifications and types of
Sounds. But at the same time, it can also record people
and people's conversations. The
mobile apps of course, are always kind
of triggering a kind of Bluetooth Wi-Fi, 3G 4G,
kind of GPS location. Maybe
that's not so bad. But you
can use the same networks to triangulate and
then pinpoint people 3D sensors.
This is a you know,
it's another way of kind of anonymizing the
The crowd monitoring types of data. So
using stereoscopic vision and
light patterns, we can basically detect movements
and and and count activities,
and shopping street or sidewalk without
using video. CCTV is
the oldest form
of data collection. And that's just video
recorders on the street recording. Everything all the time. Do
we need those things in the future? What happens if
Those are compromised. What could people do with those cameras? Wi-Fi,
Stanford's and beacons? Of course, are
targeting specific Mac addresses at
once. The Bluetooth or Wi-Fi is turned on. You
have whole new insights of who these characters are
like where these phones come from. Are they local or International
carriers? So
we have to be very, very conscious about
the different types of devices that we have to play into streets.
This is a Kind of Wonderful kind of
diagram that I made for the city of Amsterdam and it's called The
invasiveness Matrix and it explores the differences activities
about the data collection that's going on in our cities. So
on the, on the in this table,
the rows that you see here are the various types of
sensors that we can be deploying in the city. And
the columns, describe the levels of
invasiveness in terms of what kinds of
data. These devices collect.
So if you are an IT security
advisor or engineer, there
are certain types of sensors that collect
different types of metadata, contextual data,
biometric data, and even object data. And
the object data gets pretty explicit
where we can detect age and
race, and sex, and
even Brands and hats and bags, and backpacks bikes and types
of scooter. So, we can drill really down.
We can drill quickly into these kinds of things. So we want to
make sure that data protection that
privacy, is there in
the choice of your type of
data collection device.
Likewise when choosing sensors, we
can actually plot them out on a kind of Matrix
to explore the insights versus
the invasiveness. And I think this is
a healthy exercise, that every city should
do is, especially you as a,
i t, as a cyber security expert to
base those choices on the
degrees of invasiveness versus the insights. So
obviously the sweets part in this diagram is
of
Course somewhere between non-invasive
and super valuable insights because
we don't want to be you don't want to be putting your city or
the situation. Be putting themselves in a situation where they
are collecting lots of super personal
information that could be breached
or stolen in some way that
we can still get the insights that we need without compromising
the Integrity of the digital rights
and privacy of citizens.
So, this concludes the, the
four topics that I wanted to cover with you. So
a Smart Energy Future, mobility iot,
and open data. There are many, many more aspects
to Smart cities, but I
think this definitely these last four sections cover
the topics in a more
relevant. Wait for you as
an IT expert and hopefully inspire
you to roll up your sleeves and
help you.
Cities tackle, these types of dilemmas and
provide those solutions for the future.