Generate a summary report with aggregate reporting
Summary reports are a powerful tool for examining your data from Attribution Reporting and Private Aggregation. This pathway will take you through the key concepts and tooling needed to enable you to build effective reports.
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6 działania
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Introduction to summary reports
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Learn the key concepts to work with summary reports and design your data collection.
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Contribution budget for summary reports
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Article
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Learn about the role of the contribution budget for Attribution Reporting summary reports and how to allocate it to capture the data you need.
Learn how to use Noise Lab, a tool that helps grasp the effects of various noise parameters, and that enables quick exploration and assessment of various noise management strategies.
[null,null,[],[[["Attribution Reporting uses a contribution budget to protect user privacy by limiting the value associated with a single ad interaction."],["The contribution budget is a hard cap (currently 65,536), meaning no further data is recorded once it's reached, potentially impacting reporting if exceeded."],["To maximize data capture, carefully allocate the contribution budget across different metrics being tracked for a source event."],["Adjust and scale aggregatable values to the contribution budget to enhance signal quality and mitigate noise in the summary reports."],["Experiment with the API and engage with resources to optimize its use and provide feedback for further development."]]],["To minimize noise impact in aggregatable reports, scale up values before aggregation by multiplying them with a calculated scaling factor (contribution budget divided by the maximum aggregatable value). After aggregation, scale down the received summary value. Split the budget across measurement goals, allocating different scaling factors. Use coarser aggregation keys to reduce noise. Summing summary values also sums noise. Aggregate over longer time periods to increase the signal-to-noise ratio. Adjust epsilon for different noise and privacy level. Use filtering and deduplication.\n"]]
