Behavioral Biometric Cookies for Cross-Device User Tracking
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Solution Overview
Problem
Conventional cookie systems fail to reliably track users across multiple devices, differentiate between multiple users sharing the same device, and accurately measure unique visitors, leading to incorrect tracking and privacy issues due to ease of cookie deletion, browser specificity, and inability to link usage sessions across different devices or after device changes.
Innovation Solution
The development of behavioral biometric cookies that monitor and analyze user interactions to create unique profiles, allowing differentiation between users and tracking across devices, even if cookies are deleted or changed, by generating sub-cookies and cross-device cookies based on behavioral signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional cookies are used for user tracking, then implementation is simple, but tracking reliability deteriorates due to ease of deletion and browser specificity
Solution Approach 1:
The system segments user identification into multiple components: device identifiers, browser identifiers, and behavioral biometric profiles. This segmentation allows the system to rely on multiple tracking vectors simultaneously, so if one identifier is deleted or changes, others remain to maintain tracking reliability while keeping individual components simple to implement.
Solution Approach 2:
The system changes from relying solely on static cookie parameters to incorporating dynamic behavioral parameters. By monitoring and analyzing user interaction patterns (clicking behavior, scrolling patterns, typing rhythm), the system creates behavioral biometric profiles that are difficult to delete or replicate, thereby improving tracking reliability without significantly increasing implementation complexity.
2Device complexity
If single device cookies are used, then storage is simple, but user differentiation deteriorates when multiple users share the same device
Solution Approach 1:
The system applies local quality by creating user-specific behavioral profiles stored locally on each device. Each user's interaction patterns are captured and stored as distinct behavioral biometric data on the device, allowing the system to differentiate between users based on their unique local behavioral characteristics rather than relying solely on device-level identifiers.
Solution Approach 2:
The system adds another dimension to user identification by incorporating behavioral biometric data as a new layer beyond device identifiers. This dimensional expansion allows the system to distinguish between multiple users on the same device by analyzing their unique interaction patterns, thereby improving user differentiation accuracy without significantly increasing overall system complexity.
3Ease of operation
If cookies are deleted by users, then privacy control is improved, but tracking continuity deteriorates
Solution Approach 1:
The system performs preliminary action by continuously monitoring and updating behavioral biometric profiles in the background before explicit user actions. This allows the system to maintain tracking continuity by having behavioral data already captured and processed, so even if cookies are deleted, the behavioral patterns remain recorded and can be used to re-establish tracking without significant interruption.
Solution Approach 2:
The system ensures continuity of useful action by implementing persistent behavioral monitoring that operates continuously regardless of cookie status. The behavioral biometric tracking runs in the background, continuously capturing interaction patterns, ensuring that tracking continuity is maintained even when traditional cookies are deleted by users for privacy reasons.
4Device complexity
If device-specific tracking is used, then implementation is simple, but cross-device tracking capability deteriorates
Solution Approach 1:
The system applies universality by creating behavioral biometric profiles that function across multiple devices. The same user's behavioral patterns are captured and stored in a centralized database, allowing the system to recognize and track the user across different devices. This multi-functional approach enables cross-device tracking without requiring separate implementation for each device, thereby maintaining system complexity at acceptable levels.
Solution Approach 2:
The system introduces an intermediary layer - a centralized behavioral database - that mediates between multiple devices and the tracking system. This intermediary stores and manages behavioral biometric data, allowing seamless cross-device tracking by acting as a common reference point. The intermediary simplifies the overall architecture by providing a single point of coordination rather than requiring complex peer-to-peer device communication.
Data Source
AI summary
Devices, systems, and methods of generating and managing behavioral biometric cookies. The system monitors user-interactions of a user, that are performed via an input unit of an end-user device; and extracts a set of user-specific characteristics, which are used as a behavioral profile or behavioral signature. The set of user-specific characteristics are further used as a behavioral biometric cookie data-item, allowing the system to distinguish between two human users that utilize the same electronic device; and allowing the system to distinguish between a human user and an automated script. The system further allows creation and utilization of behavioral sub-cookies that distinguish among multiple users of the same device. The system also allows creation of a cross-device behavioral cookie, to track browsing or usage history of a single user across multiple electronic devices.