Generative AI Browsing Obfuscation Against Web Tracking Profiles
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Solution Overview
Problem
Existing methods for maintaining user privacy on the web, such as HTTPS, VPNs, and browser incognito modes, are insufficient in preventing the creation of user profiles by trackers, as they can be bypassed by techniques like fingerprinting, and random noise traffic can be distinguished from real traffic.
Innovation Solution
Utilizing a local or remote generative AI model to analyze web content, determine relevant categories, and generate negatively correlated search results and browsing patterns to obfuscate user behavior, making it difficult for trackers to create accurate user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random noise traffic is injected into user browsing histories, then user privacy protection is improved, but the noise traffic can be distinguished from real traffic through analysis
Solution Approach 1:
The system creates synthetic browsing records that copy the statistical properties and patterns of real human browsing behavior. Instead of using purely random noise, the patent generates artificial browsing data that mimics the complexity and variability of genuine user activity, making it indistinguishable from real traffic to trackers.
Solution Approach 2:
The system dynamically adjusts parameters of the synthetic browsing records, such as visit frequency, time intervals, and navigation patterns, to match real user behavior statistics. By changing these parameters to reflect realistic browsing patterns rather than random values, the obfuscation becomes effective while maintaining natural traffic characteristics.
2Reliability
If HTTPS and VPN are used to encrypt user connections, then network transmission security is improved, but tracking at destination websites remains possible
Solution Approach 1:
The system applies preliminary anti-action by injecting synthetic browsing records before trackers can analyze real user behavior. This pre-established noise creates a foundation of artificial activity that confuses trackers about the user's true browsing interests, preventing accurate profile generation even when destination websites can observe traffic.
Solution Approach 2:
The system performs preliminary action by pre-generating and injecting synthetic browsing records that establish a pattern of artificial activity. This preliminary noise traffic is in place before actual user browsing occurs, creating a buffer that obscures the real browsing behavior from trackers at destination websites.
3Reliability
If incognito mode is used to avoid storing tracking artifacts, then local tracking is reduced, but fingerprinting techniques can still track users
Solution Approach 1:
The system introduces an intermediary layer of synthetic browsing records between the user's actual browsing behavior and the trackers. This intermediary noise traffic acts as a mediator that obscures the connection between user actions and tracker observations, preventing fingerprinting techniques from accurately building user profiles.
4Reliability
If Generative AI is used to create synthetic browsing records, then user behavior obfuscation is improved, but computational resources are consumed
Solution Approach 1:
The system applies partial action by generating only the necessary amount of synthetic browsing records needed to achieve effective obfuscation. Instead of creating excessive synthetic data, the patent generates a sufficient quantity of realistic noise traffic that provides adequate privacy protection while minimizing unnecessary computational resource consumption.
Data Source
AI summary
A device includes a memory, including a first artificial neural network; and a second artificial neural network; and a processor, configured to implement the first artificial neural network to generate a first output based on a first input; implement the second artificial neural network to generate second output based on a second input; wherein the first input includes or represents content of a website; wherein the first output includes a content category associated with the content of the website; wherein the second input includes the content category; and wherein the second output is negatively correlated with the content category of the second input.