In the last few years we are seeing 
an explosion of devices to track our health and fitness.
Two important reasons for the explosion are:
first, sensors are
becoming cheaper and cheaper,
and second, nearly everyone now
walks around with an 
internet gateway in their pocket.
These two facts together
mean that companies can
cheaply create devices that 
record data from sensors on the body
and ship that data to their servers.
 There, 
algorithms can learn from new data
 and process it to return
useful statistics to the user,
like calories burnt,
blood pressure, or even
cortisol levels.
This is currently the leading paradigm
in fitness and health wearable technology
Now, if the last decade of internet
has taught us something, it's that
entrusting sensitive personal data
to the cloud, 
is a guaranteed recipe for disaster.
To give one eminent
wearable-related example,
in February 2018,
hackers stole data from
150 million users
of the popular fitness app 
"MyFitnessPal"
This app tracks diet and exercise,
and it can be connected to many
wearable fitness devices,
collecting the data they generate.
If your instinctive reaction
to third parties having access 
to your health metrics is...
"So what?"
you are probably not exercising
your imagination enough.
Data gets breached all the time,
for all kind of service providers.
Combine the breaches together, 
and things get uncomfortable
very fast.
 What happens when a third party
can combine, for example,
your DNA data with your diet
and fitness metrics, 
credit data and political affiliation?
 By the way, this is not speculation,
huge leaks for all these types of data
have appeared in the news
in the last few years.
What happens is that
they can start predicting things
about you,
that you yourself might not be aware of.
Obviously not a 
desirable state of things,
especially considering 
the kind of players that would buy
data from illegal hacks.
What's the alternative?
Well, we must migrate
to service architectures
where sensitive data
is not centralized, 
but rather kept with the user.
This challenge can be tackled
by combining Edge computing
and privacy-preserving Machine Learning.
With Edge computing, we process
the data locally rather than on the cloud
in this way, user privacy
is protected, and there's no
central repository of sensitive data
there to tempt hackers and shady players
To keep improving the quality of the service
we can use recent developments in
privacy-preserving Machine Learning
algorithms to learn from
a great number of users,
while they only provide data
which can't be used to reconstruct
sensitive information.
We'll dive in more detail
on privacy-preserving Machine Learning
in a future episode of this series
This has been Giovanni, from foldAI
on "Why wearables need a new paradigm".
Thanks for watching.
