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 activities
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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,[],[[["Summary reports have added noise to protect user privacy, which can impact measurement efforts."],["Noise impact is relative and less significant for larger summary values (e.g., higher conversions or revenue)."],["You can control noise impact by configuring API usage and aggregation strategies to maintain data significance."],["The noise distribution is consistent but depends on factors like epsilon, allowing for potential adjustments."],["The Laplace distribution ensures noise is unbiased, with zero being the most likely added value."]]],["Privacy Sandbox APIs, now available to most Chrome users, use summary reports with added noise to protect privacy. Managing this noise involves strategic decisions on dimension granularity, key structures, and batching frequency. The Noise Lab tool simulates these decisions, allowing users to assess noise effects. Key strategies include scaling, outlier management, and optimizing measurement goals and budget splits to improve signal-to-noise ratios. The metrics, Average Percentage Error (APE) and Root-Mean-Square Relative Error with Threshold (RMSRE_T), assess noise levels, guiding optimal key management strategies.\n"]]
