Differential Privacy: Case Studies

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Differential PrivacyCase Studies

• Denny Lee (dennyl_at_microsoft.com),
• Microsoft SQLCAT Team Best Practices

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Case Studies

• Quantitative Case Study
• Windows Live / MSN Web Analytics data
• Qualitative Case Study
• Clinical Physicians Perspective
• Future Study
• OHSU/CORI data set to apply differential privacy
  to Healthcare setting

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Sanitization Concept

• 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 Si?fi suffices. For
  many sequences, we can often use less noise than
  Si?fi . Note that ? 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.
• The resulting translation of this stream is

0 0 1 1 1 1 0 0 0 0 1
- . 2 1 . . . . 6
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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

• Our initial case study is based on Windows Live
  user data
• 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
• WL Sampled Users Web Analytics
• Customer Churn Analytics

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Windows Live Example Report

• As per below, you can see the effect on the data

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Sampled Users Web Analytics Group

• 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.

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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
United States Male 75
United States Total 203
Canada Female 15
Canada Male 15
Canada Total 30
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Customer Churn Analysis Group

• 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.

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Discussion

• 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.

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An important component to the acceptance of
privacy algorithms is the users trust of the
data.
13
Clinical Researchers Perceptions

• A pilot qualitative study on the perceptions of
  clinical researchers was recently completed.
• It has noted three categories of six themes
• 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

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Unaffected Statistics

• 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.

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Understanding the privacy algorithms

• 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.

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Can get back to the original data

• 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.

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Understand the purpose of the privacy algorithms

• 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.

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Management ROI

• We should be limiting the number of users who
  need access to full data. So is there a good
  return-on-investment 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.

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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.
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Future Collaboration

• 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_at_microsoft.com.

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Thanks

• 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.

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Practical Privacy The SuLQ Framework

• Reference paper Practical Privacy The SuLQ
  Framework
• Conceptually, this application of privacy can be
  applied to
• Principal component analysis
• k means clustering
• ID3 algorithm
• Perceptron algorithm
• Apparently, all algorithms in the statistical
  queries learning model.