What is Differential Privacy?

What Is Differential Privacy and Why Does It Matter? 1
What Is Differential Privacy
and Why Does It Matter?

In recent years, there’s been
an explosion in the amount of
personal data being collected.

Data now flows from every
business transaction, and from our
mobile and social media activity.

Increasingly, it flows from
the sensors embedded
into our daily lives, too.

While all of this data opens the
door to a vast array of possibilities
to develop applied artificial
intelligence (AI) solutions…

Collecting and managing
that data also creates
significant privacy risks.

Fortunately, in a world where
the risks and costs associated
with privacy are on the rise,
differential privacy offers
a solution.

What Is Differential Privacy and Why Does It Matter? 88What Is Differential Privacy and Why Does It Matter?
So what exactly is
differential privacy?

It’s a mathematical definition for the privacy loss
that results to individuals when their private
information is used to create AI solutions.

It measures how effective
particular techniques are at
protecting the privacy of
individuals within a larger dataset.

One example is inserting
random noise into a dataset.

This mathematical measurement of privacy
can be used to build trust with customers
so that they’re more willing to share data.

That’s important because the
aggregation of data is critical for
many machine learning projects.

After all, the more training
data you have, the better
your models will perform.

Data aggregation can also
solve the cold -start problem
for new customers.

The cold-start problem is when
you have a new customer, but
don't yet have the data to
provide them insights.

Differential privacy solves this by
enabling insights for new customers
using data from existing ones without
compromising their privacy.

And while differential privacy has
typically been the domain of big
technology companies like:

Today, smaller companies are
starting to follow their lead.
Read the case studies

So how does
differential privacy
work?

A differentially private AI solution
may use techniques such as
injecting noise into a dataset to
protect individual privacy.

Differential privacy then provides
us a measure of how well that
noise is helping protect the privacy
of the individuals in that dataset.

The end result is a differentially private
dataset or model that can’t be reverse-
engineered by an attacker.

And, while inserting random bits into
data doesn’t completely prevent
attempts to cross-reference that
data with other sources…

It does make it virtually
impossible to identify specific
individuals with full certainty.

So if you want to implement
differential privacy,
how can you get started?

First, consider the kind of
information you’re gathering.

And then determine whether
you really need the data you’re
collecting or planning to collect.

Given the growing number of data
breaches and the rise of legislation
such as General Data Protection
Regulation (GDPR)…

If your company doesn’t
need to collect information
on individuals, you might want
to avoid doing so.

Next, have your team look at what
tools are available to support your
adoption of differential privacy.

Depending on your project focus and
requirements, options may include:
1
Uber’s open source
tool that adds
differential privacy
for SQL queries.
https://github.com/uber/sql-differential-privacy

Google’s
differentially
private reporting
tool RAPPOR.
https://github.com/google/rappor
2

3
Immuta’s differential
privacy-enabled
data science
platform.
https://www.immuta.com

4
Georgian Partner’s own
differential privacy product
for machine learning.
https://georgianpartners.com/products

Finally, differential privacy
is still an emerging area
of research.

Continue to invest in talent, and hire
strong data scientists and people with
strong foundations in mathematics.

Implementing differential privacy
successfully in your company will be
less about the lines
of code needed…

And more about
understanding and applying
complex
mathematics.

The bottom line is that
differential privacy is an
important technique in
helping protect against
privacy loss.

Adopting it early will generate
cumulative benefits over time as you
build greater levels of trust with your
customers compared to your
competitors.

For that reason, every business
dealing with data needs to invest in
understanding and adopting
this important technology.

To learn more,
download the
Download
CEO’s Guide
to Differential
Privacy

What is Differential Privacy?

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