Differential Privacy:Case Studies

Denny Lee, Microsoft SQLCAT Team | Best Practices

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

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 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.:

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

Category ValueA 36B 22… …N 102 Category Value

A 34B 23… …N 108

noise

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• 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.

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

Windows Live Example Report

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

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.

Web Analytics Group Feedback

Country VisitorsUnited States 202Canada 31

Country Gender VisitorsUnited States Female 128

Male 75Total 203

Canada Female 15Male 15Total 30

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.

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.

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.

An important component to the acceptance of privacy algorithms is

the users’ trust of the data.

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

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.

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.

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.

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.

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.

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

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

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.

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