Randomized Cohort Aggregation for Privacy-Preserving Fraud Detection
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Solution Overview
Problem
The deprecation of third-party cookies in browsers has made it difficult to select and distribute personalized digital components, leading to inefficient use of computing resources and reduced effectiveness in user tracking and fraud detection, while compromising user privacy.
Innovation Solution
Implementing a privacy-preserving web activity monitoring mechanism using randomized cohorts, which assign a randomly selected identifier and timestamp to user devices, ensuring k-anonymity by grouping users into cohorts, allowing for statistical analysis and fraud detection without compromising individual privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If third-party cookies are used for user tracking and personalized content delivery, then user privacy is compromised and identification precision is too high, but if cookies are deprecated, then fraud detection capability and personalized content delivery are reduced
Solution Approach 1:
The patent segments user identification into two levels: randomized cohort identifiers for statistical aggregation and fraud detection, and device-specific identifiers for individual tracking. This segmentation allows the system to maintain fraud detection capability through cohort-level analysis while preventing individual user identification, thus resolving the contradiction between reliable fraud detection and user privacy protection.
Solution Approach 2:
The patent introduces randomized cohorts as an intermediary between users and the tracking system. Instead of directly tracking individual users with cookies, the system assigns users to randomized cohorts and tracks cohort-level behavior. This intermediary mechanism enables fraud detection through statistical analysis of cohort patterns while obscuring individual user identities, thereby maintaining reliability without compromising privacy.
2Productivity
If precise user identification is maintained for personalized content delivery, then content relevance is improved, but user privacy is compromised and users feel easily identified
Solution Approach 1:
The patent segments identification precision by implementing randomized cohorts that aggregate multiple users. Content delivery is personalized based on cohort-level characteristics rather than individual user profiles, maintaining productivity through effective personalization while reducing the loss of information by preventing precise individual identification. Users within the same cohort receive similar personalized content without being individually identifiable.
3Object-affected harmful factors
If third-party cookies are deprecated in browsers, then user privacy is protected, but digital component selection and distribution efficiency is reduced
Solution Approach 1:
The patent introduces randomized cohorts as an intermediary mechanism that replaces third-party cookies. The cohort assignment system enables digital component selection and distribution based on aggregated cohort data rather than individual cookies, protecting user privacy by eliminating direct tracking while maintaining distribution efficiency through cohort-level personalization and targeted content delivery.
Data Source
AI summary
This disclosure relates to a method for privacy-preserving web activity monitoring including receiving, from an application on a user device of a user, a request for digital content from a domain, assigning, to the application and at a first time, a randomized cohort constructed based on a randomly selected identifier and a timestamp indicating the first time at which the randomized cohort was assigned to the application, and providing, to the application and at the first time, (i) a digitally signed certificate corresponding to the randomly selected identifier and the timestamp and (ii) a unique public key and corresponding unique private key associated with the certificate, wherein the randomly selected identifier is also assigned to at least a threshold number of other applications executing on other user devices within a predetermined period of time of the assignment of the randomized cohort to the application.