Analytics Data Integrity via Encrypted Trust Tokens
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Solution Overview
Problem
Publishers face challenges in distinguishing between authentic human traffic and fraudulent traffic from malicious entities, such as bots, which contaminates analytics data and leads to inaccurate decision-making.
Innovation Solution
A method involving a token with a trust score generated using a reCAPTCHA algorithm, encrypted and decrypted to determine the trustworthiness of analytics data, allowing for the separation and processing of authentic and fraudulent traffic, enabling more informed decision-making and improved data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all analytics data is processed without verification, then data processing volume is high, but data accuracy deteriorates due to inclusion of fraudulent traffic
Solution Approach 1:
The system performs preliminary verification by decrypting tokens and validating analytics data before processing, using cryptographic verification to identify fraudulent traffic early in the data pipeline, preventing contaminated data from entering subsequent processing stages
Solution Approach 2:
The system extracts and separates fraudulent analytics data from legitimate data streams by verifying cryptographic tokens, isolating only verified data for further processing and analysis, thereby maintaining processing efficiency while ensuring data accuracy
2Reliability
If cryptographic verification is performed on all analytics data, then data trustworthiness is improved, but processing complexity increases
Solution Approach 1:
The system introduces cryptographic tokens as intermediary verification mechanisms that carry trust information from data sources, allowing verification without direct complex analysis of each data point, simplifying the overall verification process while maintaining reliability
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.