Synthetic Web Traffic Obfuscation for Digital Fingerprint Anonymity
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Solution Overview
Problem
Existing methods for maintaining online anonymity are inadequate, as companies continue to track users' activities effectively, and there is a strong desire from individuals to remain anonymous or make their data unidentifiable.
Innovation Solution
A computer-implemented method using machine learning algorithms to classify web traffic data into personas and generate synthetic online activity, which is weaved with real-world activity to deceive web trackers, including phases of acquisition, curation, and generation to obfuscate the user's digital fingerprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained to classify web traffic data into personas and generate synthetic traffic data, then the accuracy of behavioral identity classification is improved, but the complexity of the system increases
Solution Approach 1:
The patent creates synthetic copies of user traffic patterns based on classified personas. The machine learning model generates artificial traffic data that mimics real user behavior patterns, allowing the system to analyze and classify behavioral identities without processing actual sensitive user data, thus improving classification accuracy while managing complexity through data synthesis
Solution Approach 2:
The system transforms real traffic data into synthetic data by changing parameters such as timing, frequency, and pattern characteristics while preserving the essential persona characteristics. This parameter transformation allows the system to maintain classification accuracy by working with synthesized versions of real data that capture behavioral patterns without exposing sensitive information
2Reliability
If synthetic traffic data is generated to obfuscate digital fingerprints, then user anonymity is improved, but the quantity of data processing increases
Solution Approach 1:
The system generates synthetic copies of traffic data that replicate real user behavior patterns. These synthetic data copies are then mixed with real data to obfuscate digital fingerprints. By using copies rather than processing all original data, the system maintains anonymity while reducing the effective quantity of data that needs to be processed and stored
Solution Approach 2:
Synthetic traffic data acts as an intermediary between real user data and the analysis system. The synthetic data preserves the necessary behavioral patterns for classification while serving as a buffer that protects real user identities. This intermediary approach allows the system to work with larger datasets for better classification without directly processing sensitive real data
3Object-generated harmful factors
If real and synthetic traffic data are combined to deceive web trackers, then the effectiveness of fingerprint obfuscation is improved, but the difficulty of detecting and measuring real user behavior increases
Solution Approach 1:
The system takes the challenge of detecting real user behavior and converts it into a benefit by using machine learning to classify personas from the mixed data. The synthetic data, which initially makes detection difficult, becomes the basis for creating accurate persona classifications that improve the system's ability to understand and protect user anonymity
Solution Approach 2:
The synthetic traffic data serves as an intermediary that obscures the boundary between real and artificial data. Web trackers receive mixed data where synthetic portions mask real user patterns, making detection difficult. Simultaneously, the machine learning model uses this mixed data to identify persona characteristics, turning the obfuscation challenge into an improved classification opportunity
Data Source
AI summary
A computer-implemented method for digital fingerprint obfuscation is disclosed. The computer-implemented method includes training a machine learning model to classify web traffic data into one or more personas. The computer-implemented method further includes identifying, using the trained machine learning model, a particular persona of a user based, at least in part, on a user's real traffic data generated during a current user session. The computer-implemented method further includes generating synthetic traffic data based, at least in part, on the identified particular persona of the user.