Attribution Report Integration Using Privacy-Preserving Matrix Optimization
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Solution Overview
Problem
Existing attribution reporting systems face challenges in integrating diverse and conflicting interaction data from multiple sources due to privacy protection techniques, leading to inaccurate and incomplete analysis, while traditional API-based approaches lack flexibility and fail to resolve conflicts effectively.
Innovation Solution
A data-driven optimization framework is applied to integrate attribution reports and raw affirmative action data, using a matrix optimization approach that combines denoised event and aggregated counts to resolve discrepancies and enhance accuracy while preserving privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If data anonymization and aggregation are applied to protect user privacy, then user privacy is protected, but the accuracy and completeness of interaction data are reduced
Solution Approach 1:
The patent introduces an optimization framework as an intermediary layer between privacy-protected attribution reports and raw affirmative action data. This framework uses mathematical optimization to reconcile the noisy, anonymized data with raw data, producing accurate interaction measurements without exposing user privacy. The optimization process acts as a mediator that transforms protected data into useful insights while maintaining confidentiality.
Solution Approach 2:
The patent changes the parameters of data representation by transforming anonymized attribution reports through denoising operations and optimization algorithms. By adjusting parameters such as noise levels, aggregation granularity, and optimization objectives, the system recovers accurate interaction data from privacy-protected inputs without reversing the anonymization process itself.
2Quantity of substance
If multiple data sources with different granularities and noise levels are integrated, then data completeness is improved, but data conflicts and integration complexity increase
Solution Approach 1:
The patent segments the integration process into distinct stages: (1) receiving and standardizing data from multiple sources, (2) denoising individual data streams, (3) constructing the optimization model, and (4) resolving conflicts through optimization. This segmentation manages complexity by breaking down the integration task into manageable steps, each handling specific aspects of data reconciliation.
Solution Approach 2:
The optimization framework incorporates feedback mechanisms where the system iteratively adjusts its integration approach based on conflicts detected between different data sources. The optimization process uses feedback from data discrepancies to refine the combined dataset, continuously improving integration quality while managing complexity through structured iteration.
3Ease of operation
If traditional API-based approaches are used for data collection, then data collection is simplified, but flexibility and conflict resolution capability are reduced
Solution Approach 1:
The patent creates a universal optimization framework that can process multiple types of data sources (attribution reports, event-level data, raw affirmative action data) through a single integrated approach. This multi-functional system maintains the simplicity of traditional API collection while adding the flexibility to handle diverse data formats and conflict scenarios, making the system adaptable to various integration needs.
4Measurement precision
If denoising and optimization processes are applied to resolve data conflicts, then data accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial denoising and optimization to only the necessary portions of the data pipeline. Rather than fully processing all raw data through complex optimization, the system selectively applies denoising to attribution reports and uses optimization only where conflicts exist between data sources. This partial action approach maintains accuracy where needed while reducing unnecessary computational overhead.
Data Source
AI summary
The disclosure generally describes methods, software, and systems for integration of attribution reports and auxiliary data sources. Data including aggregated summary reports and event level reports is received from a first system. Additional raw affirmative action data related to the aggregated summary reports and event level reports is received from a second system. A matrix is created with rows for interaction events and columns for raw affirmative action data. Denoised aggregated counts and denoised event counts are determined from the received reports. The matrix fields are optimized by resolving conflicts between raw counts, denoised aggregated counts, and denoised event counts.