Privacy-Preserving Unique Engagement Estimation via Group Tracking
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Solution Overview
Problem
Conventional systems that track user engagement at the group level struggle to accurately measure unique engagements with content, as they cannot distinguish between single users engaging multiple times and unique users engaging with the content.
Innovation Solution
The system calculates an estimated unique engagement count using group identity tracking data and provides a probabilistic guarantee score to assess the accuracy of this estimate, ensuring user privacy is maintained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user engagement is tracked at the group level to protect individual user privacy, then user privacy is protected, but the ability to accurately measure unique engagements is lost
Solution Approach 1:
The patent introduces probabilistic guarantee scores as an intermediary mechanism that bridges group-level tracking and individual-level measurement accuracy. The system uses statistical methods to calculate and report confidence scores alongside engagement metrics, allowing content providers to assess the reliability of unique engagement measurements while maintaining group-level privacy protection. This intermediary layer enables both privacy preservation and measurement accuracy without requiring individual user identification.
2Measurement precision
If individual user tracking is implemented to accurately measure unique engagements, then measurement precision is improved, but user privacy is compromised
Solution Approach 1:
The patent extracts the essential measurement capability from individual user tracking by using statistical inference on group-level data. Instead of tracking individual users, the system extracts unique engagement metrics through probabilistic calculations that work with aggregated group data. This extraction approach removes the need for individual identification while preserving the core measurement function, thereby maintaining privacy while achieving measurement precision.
3Reliability
If group identity tracking is used instead of individual user tracking, then user privacy is protected, but the system cannot distinguish between single users engaging multiple times and unique users engaging with content
Solution Approach 1:
The patent implements feedback mechanisms that provide content providers with probabilistic guarantee scores alongside engagement metrics. This feedback loop allows content providers to understand the confidence level of unique engagement measurements and adjust their content strategies accordingly. The system continuously refines its statistical models based on accumulated data, improving the accuracy of engagement pattern inference while maintaining group-level anonymity. This feedback-driven approach recovers lost information about engagement patterns without compromising privacy.
Data Source
AI summary
Embodiments of the disclosed technologies are capable of providing engagement feedback for an online system while maintaining user privacy. Embodiments determine one or more user groups. A user group includes users, and a size of the user group is selected to protect user privacy. A user action is received from a user in a first group. The user action includes an interaction with a piece of content. A group action count is updated for the first group based on the user action. The group action count indicates a number of interactions with the piece of content by any of the users in the first group. An estimated number of unique engagements is calculated based on the group action count and the size of the first group. The estimated number of unique engagements is provided to the online system as the engagement feedback for the piece of content.