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Workloads of Counting Queries: Enabling Rich Statistical Analyses With Differential Privacy

Guest author from academia walks us through considerations and tools available to conduct this type of analysis with differential privacy

Ryan McKenna
Tue, 06/08/2021 - 12:02
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All articles in this series
Differential Privacy for Privacy-Preserving Data Analysis
Threat Models for Differential Privacy
Counting Queries: Extracting Key Business Metrics From Datasets
Summation and Average Queries: Detecting Trends in Your Data
Workloads of Counting Queries: Enabling Rich Statistical Analyses With Differential Privacy
Differential Privacy Bugs and Why They’re Hard to Find
Body

To date, this series focused on relatively simple data analyses, such as learning one summary statistic about our data at a time. In reality, we’re often interested in a slightly more sophisticated analysis, so we can learn multiple trends and takeaways at once and paint a richer picture of our data.

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In this article, we will look at answering a collection of counting queries—which we call a workload—under differential privacy. This has been the subject of considerable research effort because it captures several interesting and important statistical tasks. By analyzing the specific workload queries carefully, we can design very effective mechanisms for this task that achieve low error.

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