Fuzzy Device Identifier Assignment for Real-Time Fingerprinting
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Solution Overview
Problem
Current device fingerprinting technologies face challenges in evaluating the quality of device fingerprinting algorithms due to the lack of ground truth labels, and they struggle with achieving accurate results in real-time due to high traffic volumes and skewed device visit distributions.
Innovation Solution
The use of fuzzy device identifiers (IDs) based on device attributes such as operating system, web browser, screen resolution, and others, which are compared using similarity metrics to assign device IDs to events, and the application of logistic regression models and attribute encoding matrices to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional device fingerprinting algorithms are used to match devices in real-time, then processing speed is improved, but measurement precision deteriorates due to lack of ground truth labels and skewed device visit distributions
Solution Approach 1:
The patent introduces fuzzy device identifiers as an intermediary representation between raw device attributes and ground truth labels. These fuzzy IDs enable real-time processing by creating a layered approach where device attributes are first transformed into fuzzy identifiers, which can then be matched without requiring direct access to ground truth labels for every comparison, thus resolving the contradiction between speed and precision.
Solution Approach 2:
The patent creates a simplified copy of device identification through fuzzy device identifiers that capture essential characteristics without requiring complete ground truth. This copying approach allows for fast matching operations while maintaining sufficient accuracy for practical purposes, addressing the real-time processing requirement without sacrificing measurement precision entirely.
2Measurement precision
If fuzzy device identifiers are assigned based on similarity metrics, then device fingerprinting accuracy is improved, but device complexity increases due to multiple attributes and comparison operations
Solution Approach 1:
The patent segments the device identification process into distinct components: collecting device attributes, transforming them into fuzzy device identifiers, and performing similarity comparisons. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high accuracy through specialized processing at each stage.
Solution Approach 2:
The patent changes the representation parameters of device identifiers from raw attribute values to transformed fuzzy identifiers that encode similarity information. This parameter transformation simplifies the comparison operations needed for matching, as the fuzzy identifiers directly represent the similarity metrics rather than requiring complex multi-attribute comparisons, thus reducing device complexity while improving accuracy.
3Measurement precision
If ground truth labels are used for device identification, then measurement precision is improved, but loss of information increases due to availability limitations and skewed distributions
Solution Approach 1:
The patent uses fuzzy device identifiers as an intermediary that bridges the gap between available device attributes and ground truth labels. These fuzzy IDs enable accurate identification even when ground truth is incomplete or skewed, as they capture the essential similarity information needed for matching without requiring complete ground truth data for all devices, thus reducing information loss.
Solution Approach 2:
The patent performs preliminary transformation of device attributes into fuzzy device identifiers before the actual identification process. This preliminary action prepares the data in a format that is more resilient to ground truth limitations and skewed distributions, allowing accurate identification to proceed even with incomplete or biased ground truth information.
Data Source
AI summary
Various device attributes associated with a current event may be obtained. Similarity metrics may be determined that indicate a degree of similarity between the device attributes that are associated with the current event and stored device attributes that are associated with previous events and previously created fuzzy device identifiers. A fuzzy device identifier may be assigned to the current event based at least in part on a comparison of the similarity metrics with a threshold. If none of the similarity metrics compare favorably with the threshold, then a new fuzzy device identifier may be created for the current event. However, if at least one of the similarity metrics compares favorably with the threshold, then the previously created fuzzy device identifier whose stored device attributes are most similar to the device attributes that are associated with the current event may be assigned to the current event.