Hello, everyone.
Welcome back.
This is Ramesh Raskar, associate professor at MIT Media Lab.
I'm going to talk about contact tracing and reopening businesses with Safe Paths.
Some of the work I described is also being done by the nonprofit Path Check.
Today, I'm going to focus on the role of machine learning as we try to create a global AI that
still is privacy-preserving for an individual.
Safe Paths is a privacy-preserving platform, which on the left side, as you can see, is
phasing the citizens a Safe Paths app that's for exposure notification, contact tracing
information, access to resources, and, very soon, symptom tracking, quarantine verification,
and other validation.
And then on the right is a solution for public health and businesses, which is a dashboard
that can expedite tracing conversations, improve the quality of data, but, very soon, can also
create dashboards with hotspots and predict the spread, coordinate population at home,
and, of course, create solutions for certification.
In that sense, Safe Paths is one of the largest open source effort for COVID-19.
We use proximity technologies, such as GPS and Bluetooth or Apple, Google, and we'll
also support the protocols like PACT from MIT in the future and other modalities.
We want to achieve all of this without infringing on personal freedom.
In that sense, Safe Paths has three pillars.
The technology pillar, where there's a flagship app, but they're also helping everybody else
create their apps as well.
A backends and dashboards and web tools.
A lot of algorithms we publish on a regular basis as the case, and privacy and interoperability
are the two main aspects of the technology.
Then, for the think tank, we are publishing standards, privacy, and ethics guidelines.
We convene stakeholders, we do landscape analysis, and then implementation, which is kind of
last-mile efforts, which are mainly conducted by Path Check the foundation, about public
health engagement, straining, monitoring, bringing solution providers and solution seekers
together, such as startups and relationships with corporations for the software deployment.
Safe Paths has three broad audiences.
For citizens is, of course, the app and the self-assessment and information.
The emphasis for this particular call is about a relationship with public health and businesses.
Public health, as I said, we have open source code and we're helping manage jurisdictions,
deploy their own apps and their own dashboards.
Our goal is to really support existing manual contact tracing operations and amplify and
simplify the efforts with interoperable backbone.
The Safe Paths alliance, of course, has fantastic MIT faculty and it's also shaped by conversations
with WHO and HHS and partners like Mayo Clinic and EY and so on.
The beauty is that a lot of the APIs that are being released by organizations, including
Google and Apple, for, in this case, a Bluetooth API that allows individuals to figure out
if they came in proximity of an infected persons, but able to embed [inaudible 00:03:33] APIs
and they're privacy-preserving.
But if you look at the experience flow for an individual through COVID-19, either unhealthy
individual or infected or an exposed person, those flows are actually very complex.
The exposure notification is just a small part of it, but then there's contact tracing,
which is the ability to talk to the person who may have been exposed.
There's triaging, there is need to testing or isolation, telehealth, quarantine verification,
have you maintained the quarantine, talking to public health who can authorize certain
tests or certain certificates, and then also kind of creating a closed-loop system for
public health.
The challenge is how can it resolve all of this?
How can we do all of this while preserving privacy at every stage?
And that means without knowing the name, phone number, email address, or some kind of a national
ID number, how can we support most of these processes?
It's privacy-preserving kind of to avoid a big brother surveillance system, but, of course,
information is required for any particular entity, but we want to avoid slipping into
a big brother scenario.
By doing that, hopefully we can improve the adoption and we don't infringe in on personal
freedom.
The population that's marginalized or are fearful of law enforcement, are usually discriminated
against, will feel like they can participate in the system.
It's a win-win if we can create a privacy-preserving solution at every stage.
So, today, I'm going to talk about how privacy-preserving happens, not just at exposure notification
and contact tracing, but also later on, which requires a lot of machine learning tools,
as you can imagine.
Privacy is a single word that encompasses multiple ideas and poses the notion of consent,
regulations like HIPAA, straight secrets that prevents a hospital from working with insurance
player versus a pharmaceutical company, and also national security, because a social graph
of a country and a health of individuals is also a national secret.
You don't want to centralize that and subject that to a data breach.
At the end of the day, privacy also includes the notion of incentive.
How can we encourage healthy young people to participate in the system, because there
should be something in it for them as well?
You can have a similar experience flow for businesses as well.
Whether you need a solution at the entry point of your factories and workplaces, how the
employees enter the office, how do you schedule the meetings and [inaudible 00:06:19] them
and so on.
Again, we want to achieve all of this in a privacy-preserving way.
If you see our website, we are publishing the white papers, which have very rapid frequency
of how they are location context with encryption.
How can you use WiFi colocation?
What are some privacy guidelines?
How can you use Bluetooth in a multi-hop analysis?
If you use GPS only, is there privacy-preserving intersections you can do?
Techniques like split learning for distributed and private machine learning.
If writing apps, what could be the unintended consequences and so on.
So, I encourage you to look at the website.
We are writing the white papers, even before they're published.
Coming back to machine learning aspect of it, if you want to achieve all these goals,
let's say you have a symptom checker, let's say you have a particular questionnaire on
your phone, or you have a diagnostic test on the phone.
How can you create machine learning models without individuals realizing everything about
their own information?
This problem boils down to creating a distributed and private machine learning solution.
Here on the horizontal axis, we have abilities to protect data.
Anonymization, as many of you know, is not the same as privacy and is an overlapping
data sets.
You can actually reidentify the individuals.
So, anonymization, let's consider as the lowest possible way to protect data.
The next idea is to obfuscate, which is add some noise using techniques such as differential
privacy.
For the best method to protect data is to encrypt it with techniques such as homomorphic
encryption.
But the challenge with that is if you start protecting your data, the utility, which is
shown on the vertical axis, also goes down.
If you encrypt, then you can also do statistical analysis on your data, but you cannot run
inference or train full-fledged machine learning models.
A few new matters have emerged in the last few years that allow you to train machine
learning models without exchanging raw patient data.
One is called split learning and the other one is called federated learning.
The idea behind both these approaches is [inaudible 00:08:27] the raw data from the phones or
any medical records.
How can it share some wisdom, some kind of a collective intelligence or a feature space,
and share wisdom without raw data?
Let's look at these two approaches very quickly.
The first one is federated learning proposed by Google.
It's a fantastic method that takes the data that you have.
Think of the clients here as smartphones of the users with COVID, and they can go through
a symptom checker or questionnaires, and the raw answers never get uploaded to the server,
but you train a local machine learning model and the machine learning model gets better
and better and starts sharing just the weights of those machine learning models to the server.
The server can take all this partially trained models and effectively average them at the
server and then becomes a better model than a model at any one of those phones.
This process continues with a few cycles and eventually you get a great, great model that
you want to train.
Split learning, which is a technique from our group at MIT Media Lab, uses a slightly
different approach, which is still for distributed machine learning, which is you convert each
answer from this phone into an intermediary representation, which we call smashed data
or smashed knowledge.
Only the smashed knowledge is shared with the server.
So, the server doesn't know anything about the raw data at the client devices, but only
knows this intermediate representation.
And again, the key idea here is that intermediary representation, the smashed data, does not
reveal much about the input.
We think by doing a mathematical analysis of what data gets leaked in those representations,
we can give some mathematical guarantees on how this metrics could work.
So, please look at how we reduce the so-called data leakage in techniques, such as federated
learning or split learning.
The key benefit of techniques like split learning or federated learning is that compared to
traditional machine learning methods, first of all, they get great privacy.
Second, you can simultaneously reduce the computation and bandwidth loads as well, right?
That's key for techniques like federated and split learning.
In somebody, now we have an opportunity to attack COVID-19 not just for contact tracing
and for exposure notification, but you can do this for everything else.
For triaging, where there may be questionnaires or self-assessment, or for quarantine verification
by using sensors on the phone, the video, audio, GPS, all the sensors on the phone,
and we can put all of that into a machine learning model to provide better and better
services.
This is the kind of work we are doing at MIT Media Lab as time goes on, as it move beyond
the contact tracing aspect of it.
If you're a public health or a business entity and thinking about ID solution, we think it's
not just enough to create a contact tracing solution or a machine learning solution, but
should be privacy first.
It should be open source, so we can avoid [vendor block 00:00:11:29], it's interoperable,
supports multiple languages, supports non-smartphones, and really is geared primarily for enhancing,
simplifying, and amplifying the public health infrastructure using dashboards for hotspots
and spread analysis.
We have many papers on our website that you can take a look at.
At the end of the day, what we want to do is create a bird's eye view for governments
and public health and businesses so that they can choreograph the challenges faced by the
COVID-19 disruptions.
But if you create a bird's eye view that can easily lead to a certain state if you start
using digital tools.
So, what we also need is a bottom-up coordination amongst communities, but they should be able
to coordinate with each other in a privacy-preserving way and also help the public health entities
and businesses.
We think open source, nonprofit solutions like Safe Paths and the algorithms and technologies
we have at MIT Media Lab will, in the short term, address the public health problems.
In the medium term, help us restart the economy in a safe way and over time, also build resilient
societies.
Thank you.