Browser Fingerprinting Uniqueness and Processing Time Optimization
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Solution Overview
Problem
Existing methods for identifying trustworthy computing devices in online communications, such as browser fingerprinting, face challenges in balancing uniqueness and processing time, with users often deleting cookies and traditional fingerprinting methods being limited in uniqueness and efficiency.
Innovation Solution
A system and method that involves transmitting fingerprinting tests to computing devices, analyzing test results for uniqueness and time performance, and aggregating data to determine an ideal balance between uniqueness and processing time, using techniques like canvas fingerprinting combined with other methods to enhance identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional browser fingerprinting methods are used to identify computing devices, then device identification can be achieved, but the uniqueness is limited and users can delete cookies to defeat identification
Solution Approach 1:
The patent segments the fingerprinting process into multiple independent components: canvas rendering tests, user agent analysis, and cookie-based identification. Each component serves a specific purpose and can be combined to achieve higher overall uniqueness and reliability, making it harder for users to completely evade identification by deleting just one type of identifier.
Solution Approach 2:
The patent merges multiple fingerprinting techniques into a unified system that combines canvas-based rendering tests, user agent string analysis, and traditional cookie identification. This combination creates a more robust identification system where the strength of one method compensates for weaknesses in others, achieving both high uniqueness and reliability.
2Measurement precision
If enhanced fingerprinting techniques are applied to improve uniqueness, then device identification accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by implementing fingerprinting tests selectively based on the required level of identification. The system can perform quick user agent analysis for low-stakes scenarios and reserve more time-consuming canvas rendering tests for high-security situations, achieving acceptable uniqueness without always incurring maximum processing time costs.
Solution Approach 2:
The patent changes the parameters of fingerprinting tests dynamically, adjusting the complexity and duration of tests based on the specific identification needs. By varying test parameters such as canvas rendering complexity and the number of fingerprinting components analyzed, the system optimizes the balance between uniqueness and processing time for different应用场景.
3Reliability
If multiple fingerprinting tests are run to improve identification accuracy, then uniqueness improves, but the time required for processing increases
Solution Approach 1:
The patent implements dynamic fingerprinting test selection where the system adjusts the number and type of tests based on real-time needs. The fingerprinting process is not static but adapts its complexity based on the specific identification requirements, achieving high reliability when needed while maintaining efficiency for routine operations.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system evaluates the results of initial fingerprinting tests and adjusts subsequent testing accordingly. If early tests provide sufficient identification confidence, the system can stop there, avoiding unnecessary additional tests and maintaining high productivity while achieving adequate reliability.
Data Source
AI summary
A processor-implemented method for improving computer identification comprising transmitting a browser fingerprinting test to one or more computing devices and receiving test data from the one more computing devices that includes at least an elapsed processing time and a hash code. The method includes determining an average elapsed processing time and determining a uniqueness level for the browser fingerprinting test by comparing the hash codes for each computing device with one another.