Differential Privacy:
Case Studies
Denny Lee (dennyl@microsoft.com),
Microsoft SQLCAT Team | Best Practices
Case Studies
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Quantitative Case Study:
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Qualitative Case Study:
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Windows Live / MSN Web Analytics data
Clinical Physicians Perspective
Future Study
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OHSU/CORI data set to apply differential privacy to
Healthcare setting
Sanitization Concept
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Mask individuals within the data by creating a sanitization
point between user interface and data.
The magnitude of the noise is given by the theorem. If many
queries f1, f2, … are to be made, noise proportional to ΣiΔfi
suffices. For many sequences, we can often use less noise
than ΣiΔfi . Note that Δ Histogram = 1, independent of
number of cells
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Generating the noise

To generate the noise, a pseudo-random number
generator will create a stream of numbers, e.g.:
0

0
1
1
1
…
1
0
0
0
0
1
.
6
The resulting translation of this stream is:
-
.
2
+
1
…
+
.
.
.
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Adding noise
•
•
•
The stream of numbers above is applied
to the result set.
While masking the individuals, it allows
accurate percentages and trending.
Presuming the magnitude is small (i.e.
small error), the numbers are
themselves accurate within an
acceptable margin.
Category
Value
A
36
B
22
…
…
N
102
noise
Category
Value
A
34
B
23
…
…
N
108
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Windows Live User Data
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Our initial case study is based on Windows Live user
data:
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550 million Passport users
Passport has web site visitor self-reported data: gender, birth date,
occupation, country, zip code, etc.
Web data has: IP address, pages viewed, page view duration, browser,
operating system, etc.
Created two groups for this case study to study the
acceptability / applicability of differential privacy within the WL
reporting context:
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WL Sampled Users Web Analytics
Customer Churn Analytics
Windows Live Example Report
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As per below, you can see the effect on the data
Sampled Users Web Analytics
Group
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New solution built on top of an existing Windows
Live web analytics solution to provide a sample
specific to Passport users.
Built on top of an OLAP database to provide analysts
to view the data from multiple dimensions.
Built as well to showcase the privacy preserving
histogram for various teams including Channels,
Search, and Money.
Web Analytics Group Feedback



Feedback was negative because customers
could not accept any amount of error.
This group had been using reporting
systems for over two years that had
perceived accuracy issues.
They were adamant that all of the totals
matched; the difference on the right was
not acceptable even though this data was
not used for financial reconciliation.
Country
Visitors
United States
202
Canada
31
Country
Gender
Visitors
United States
Female
128
Male
75
Total
203
Female
15
Male
15
Total
30
Canada
Customer Churn Analysis
Group
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This reporting solution provided an OLAP cube, based on an
existing targeted marketing system, to allow analysts to
understand how services (Messenger, Mail, Search, Spaces,
etc.) are being used.
A key difference between the groups is that this group did not
have access to any reporting (though it was requested for
many months).
Within a few weeks of their initial request, CCA customers
received a working beta in which they were able to interact,
validate, and provide feedback to the precision and accuracy
of the data.
Discussion
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The collaborative effort lead to the customer
trusting the data, a key difference in comparison to
the first group.
Because of this trust, the small amount of error
introduced into the system to ensure customer
privacy was well within a tolerable error margin.
The CCA group is in direct marketing hence had to
deal more regularly with customer privacy.
An important component to the
acceptance of privacy algorithms is
the users’ trust of the data.
Clinical Researchers Perceptions
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A pilot qualitative study on the perceptions of clinical
researchers was recently completed.
It has noted three categories of six themes:
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Unaffected Statistics
Understanding the privacy algorithms
Can get back to the original data
Understanding the purpose of the privacy algorithms
Management ROI
Protecting Patient Privacy
Unaffected Statistics
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The most important point – no point applying privacy
if we get faulty statistics.
Primary concern is healthcare studies involve smaller
number of patients than other studies.
We are currently planning to provide in the near
future a healthcare template for the use of these
algorithms.
Understanding the privacy algorithms
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As we have done in these slides, we have described
the mathematics behind these algorithms only
briefly.
But most clinical researchers are willing to accept the
science behind them without necessarily
understanding them.
While this is good, it does pose the problem that one
will implement them w/o understanding them
incorrectly guaranteeing the privacy of patients.
Can get back to the original data
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It is very important to get back to the original data set
if so required.
Many existing privacy algorithms perturb the data so
while guaranteeing the privacy of an individual, it is
impossible to get back to the individual.
Healthcare research always requires the ability to get
back to the original data to potentially inform
patients of new outcomes.
The privacy preserving data analysis approach here
will allow this ability.
Understand the purpose of the privacy
algorithms
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Most educated healthcare professionals understand
the issues and providing case studies such as the Gov
Weld case make this more apparent.
But we will still want to provide well-worded text
and/or confidence intervals below a chart or report
that has privacy algorithms applied.
Management ROI
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We should be limiting the number of users who need
access to full data. So is there a good return-oninvestment to provide this extra step if you can
securely authorize the right people to access this
data?
This is where standards from IRB, privacy & security
steering committees, and the government get
involved.
Most importantly: the ability to share data.
Protecting Patient Privacy
For us to be able to analyze and mine
medical data so we can help patients
as well as lower the costs of
healthcare, we must first ensure
patient privacy.
Future Collaboration
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As noted above, we are currently working with OHSU
to build a template for the application of these
privacy algorithms to healthcare.
For more information and/or interest in participating
in future application research, please email Denny
Lee at dennyl@microsoft.com.
Thanks
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Thanks to Sally Allwardt for helping implement the
privacy preserving histogram algorithm used in this
case study.
Thanks to Kristina Behr, Lead Marketing Manager, for
all of her help and feedback with this case study.
3/23/2016
Practical Privacy: The SuLQ Framework
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Reference paper “Practical Privacy: The SuLQ
Framework”
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Conceptually, this application of privacy can be
applied to:
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Principal component analysis
k means clustering
ID3 algorithm
Perceptron algorithm
Apparently, all algorithms in the statistical queries learning
model.
3/23/2016