Confusion Matrix Estimation via Differential Privacy Sketches
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Solution Overview
Problem
In distributed computing environments, predicting performance metrics for prediction systems without access to ground truth information from devices is challenging, especially when dealing with private or protected information.
Innovation Solution
A method that involves serving content to client devices, predicting attributes associated with these devices, generating a data sketch descriptive of the predicted attributes, noising the data sketch to satisfy differential privacy criteria, and transmitting the noised sketch to a reference system for evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground truth information is obtained from devices to evaluate prediction quality, then measurement precision is improved, but information security deteriorates due to private or protected information exposure
Solution Approach 1:
A trusted reference system acts as an intermediary between the prediction system and ground truth data. The reference system receives predictions, compares them against ground truth locally, and returns only aggregated performance metrics without exposing raw ground truth information. This mediator approach enables accurate evaluation while maintaining information security boundaries.
Solution Approach 2:
Instead of accessing actual ground truth data from devices, the system uses a reference system that maintains copies or representations of ground truth information in a controlled environment. This allows evaluation to proceed on replicated data rather than directly on sensitive source data, reducing security risks.
2Measurement precision
If direct access to device information is implemented for evaluation, then measurement precision is improved, but device complexity increases due to additional data collection mechanisms
Solution Approach 1:
The ground truth data collection and storage functionality is extracted from individual devices and centralized in a separate reference system. Devices only need to send predictions to the reference system, which already possesses or can obtain ground truth information independently. This extraction eliminates the need for complex data collection infrastructure at each device while maintaining evaluation accuracy.
Solution Approach 2:
The reference system independently obtains and manages ground truth information without requiring direct device involvement in the evaluation process. The system serves itself by maintaining its own ground truth database and using it to evaluate predictions received from various sources, reducing the burden on device systems.
Data Source
AI summary
An example method includes: serving content to a plurality of client devices associated with a plurality of tag values; predicting, using a prediction system, a plurality of attributes respectively associated with the plurality of tag values; generating a data sketch descriptive of the plurality of predicted attributes; noising the data sketch, wherein the noised data sketch satisfies a differential privacy criterion; transmitting the noised data sketch to a reference system; and receiving, from the reference system, estimated performance data associated with the predicted attributes, wherein the estimated performance data is based on an evaluation of: reference attribute data associated with one or more of the plurality of tag values and the predicted attributes for the one or more of the plurality of tag values.