Building Trust in Synthetic Data
Key Points
- Enterprises must gauge the trustworthiness of synthetic data, especially when it replaces privacy‑restricted real data that fuels decision‑making.
- Trust can be secured through three key levers: data **quality**, privacy safeguards, and a robust **deployment** framework.
- Quality assurance involves both column‑level checks (matching distributions and preserving inter‑column correlations) and row‑level checks (ensuring logical consistency of generated records).
- Privacy controls are essential to guarantee that synthetic datasets do not inadvertently expose sensitive information or increase regulatory risk.
- A reliable deployment setup—including proper monitoring, governance, and integration pipelines—ensures synthetic data can be used confidently across business units, risk teams, and data science workflows.
Full Transcript
# Building Trust in Synthetic Data **Source:** [https://www.youtube.com/watch?v=QQtSa9ngqQk](https://www.youtube.com/watch?v=QQtSa9ngqQk) **Duration:** 00:08:08 ## Summary - Enterprises must gauge the trustworthiness of synthetic data, especially when it replaces privacy‑restricted real data that fuels decision‑making. - Trust can be secured through three key levers: data **quality**, privacy safeguards, and a robust **deployment** framework. - Quality assurance involves both column‑level checks (matching distributions and preserving inter‑column correlations) and row‑level checks (ensuring logical consistency of generated records). - Privacy controls are essential to guarantee that synthetic datasets do not inadvertently expose sensitive information or increase regulatory risk. - A reliable deployment setup—including proper monitoring, governance, and integration pipelines—ensures synthetic data can be used confidently across business units, risk teams, and data science workflows. ## Sections - [00:00:00](https://www.youtube.com/watch?v=QQtSa9ngqQk&t=0s) **Enterprise Trust in Synthetic Data** - The speaker explains how synthetic data lets companies safely unlock privacy‑restricted information for faster insights, and outlines the quality, privacy, and risk controls required for business, compliance, and data‑science teams to trust its use. ## Full Transcript
today I want to talk about trust and
this isn't trust between two people but
rather an Enterprise's ability to trust
the synthetic Tabler data that they
create now this is important it's
important because data still drives
decision making and because of that
companies collect a lot of
it they collect across a variety of
domains ranging from
Financial to customer data as well as to
Ops
data now unfortunately not all this data
can be accessed they can't get value
from all this data and that's because of
data privacy data privacy locks up a lot
of this
data and for that reason companies are
looking at leveraging synthetic data
specifically targeting the data that
can't be accessed so creating High
Fidelity data sets of their financial
data and potentially their customer
data now this is important because with
the synthetic data they'll have more
data which means more insights quicker
go to market more
Innovation but the question we hear
always is can we trust this
data synthetic data it's fake data can
we trust it now the short answer is yes
you can but it also depends if you have
the right levers in place now this is
important if you're line of business
they want to make make sure that the
data is high quality if you're in Risk
privacy and compliance they want to make
sure that this data doesn't expose them
to any more risk and if you're
Downstream a data scientist or data
Moder you also want to make sure that
data is high quality privacy protected
and can address your use case so today
we're going to talk about three levers
that companies can put in place to make
sure that they can confidently and
reliably use a synthetic data that they
generate the first will be building
trust through quality
the second will be building trust
through
privacy and the third will be building
trust through your deployment
setup now let's walk through each of
these and break them
down when we talk about quality what I'm
referring to here is how closely does
the synthetic
data align with your real data from a
statistical perspective persective and
we can look at this from two ways
one column
quality with column quality we're really
concerned with column distributions and
column correlations now distributions
making sure that distribution of the
synthetic output aligns with the real
data and the correlation is making sure
that every column correlation both one
to one and one to many also AE now often
times you'll have metrics that get
generated that give you insight into
each of these two aspects so as long as
you have that it should have trust in
the column quality the second aspect of
quality is row
quality now there are two examples here
let's say you know we want to generate
synthetic data that has customer data it
says you know he or she lives in Austin
Texas
78702 now our synthetic data output
doesn't have to align exactly with that
but there should be some logical
consistency in other words it shouldn't
say New York Alaska 78
702 the second aspect of Ro quality
refers to kind of
formulas some relationships are rigid
and have to be maintained for example if
we're leveraging financial
data we may have profit revenue and cost
which we have to maintain you want to
make sure that in your synthetic data
tool you have the ability to maintain
these relationships through formulas so
that the output can still align and be
useful Downstream
now with those two measures in place
quality you should have trusted let's
talk about
privacy with privacy this is important
because remember we're still leveraging
pii so we want to make sure that none of
this pii data gets exposed and we want
to have two things in
place the first is a mechanism to apply
to our training data to make sure that
none of this data gets exposed now it's
important to know that there's a
relationship between quality and
privacy typically the more privacy you
have the less quality you have now
traditional techniques anonymization
masking do a fantastic job at privacy
but not so great at quality but they're
all Alternatives differential privacy we
can abbreviate as
DP is one approach that can still give
you the Privacy you need while
maintaining allow the quality and
utility the second aspect of privacy is
to make sure that we have
metrics the mechanism will allow you to
apply privacy the metrics will tell you
how much risk you're potentially exposed
to now often time you'll have a metric
around
leakage leakage tells you well how much
of this real data potentially trickled
in and snuck into your synthetic data
set the lower the better but ideally you
want to take this a step further you
want understand what what's the
probability of an inference ATT Tech the
probability of a third party potentially
identifying sensitive information my
synthetic data set with these two
metrics and with the mechanisms in place
you should have pretty good confidence
in the your the privacy of your
synthetic data now let's talk about
deployment the
first question we should think about is
well should we go on Prem or should we
go cloud
a lot of companies today are shifting
their workloads to the cloud and that
makes sense there's scale there's
efficiency continuous updates but not
all workloads are meant for the cloud
with synthetic data because we're
leveraging pii a lot of companies don't
feel comfortable sending Pi data to a
third party Cloud so in this case trust
can be built through an on-prem
deployment that being said once you do
generate synthetic data that is high
quality and privacy protected you can
consider deploying those synthetic data
sets Downstream through a cloud
deployment so you could potentially
leverage both in this
case the second aspect of deployment
should we consider
centralized or
decentralized and what I mean by this is
should we let everyone create data and
consume data or should we separate those
roles given the variety of the data sets
that we see the variety thresholds from
quality and privacy across a variety of
use cases trust can better be built
through po potentially a centralized
approach in this case we're limiting it
to a group of people who generate the
data uh they make sure that has a
quality standard they work with the
privacy and risk team to make sure that
they are privacy protected and then once
these two measures are met they can then
push them to potentially a cloud for
Downstream use now how can trust be
built well first can it be trusted
absolutely that's a yes now it's
provided you have the right quality
measures in place you have the right
privacy protection in place and of
course you have the right deployment in
place as well now with those three you
are more than happy to send them
Downstream and to begin to reap all the
benefits of your synthetic data if you
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