Network Connection Request Classification for Privacy Protection
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Solution Overview
Problem
Existing methods for distinguishing main and sub-requests in network traffic are inefficient, leading to resource wastage and privacy violations due to excessive tracking, and often result in degraded user experience.
Innovation Solution
A method and apparatus for distinguishing network connection requests at a network layer, which involves detecting and classifying connection requests as main or sub-requests based on analysis of host activity, domain referrer matching, and time thresholds, thereby identifying third-party requests that can be blocked to protect user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If visitor tracking is implemented to analyze behavior and acquire data, then personalization and targeted advertising are improved, but resource wastage and privacy violations increase
Solution Approach 1:
The patent segments network traffic into main requests and sub-requests, allowing differential processing. Main requests (user-initiated) are permitted while sub-requests (tracker-generated) are blocked, enabling personalization functionality to be preserved for legitimate uses while eliminating resource wastage from excessive tracking.
Solution Approach 2:
The patent applies different quality controls to different parts of network traffic. Instead of uniform blocking or permitting, it applies specific filtering rules to sub-requests based on their characteristics (third-party domains, timing patterns), allowing local optimization of resource usage while maintaining personalization where appropriate.
2Productivity
If excessive tracking is performed to gather user data, then targeted advertising effectiveness is improved, but user privacy protection deteriorates
Solution Approach 1:
The patent extracts and removes harmful tracking sub-requests from the network traffic stream while permitting legitimate main requests to pass through. This selective extraction blocks third-party trackers that follow users across websites, protecting privacy while maintaining the functionality of user-initiated browsing and legitimate personalization.
Solution Approach 2:
The patent converts the harmful effect of excessive tracking into a benefit by using traffic analysis to distinguish between legitimate personalization requests and malicious tracking. The filtering mechanism itself becomes a protective feature that enhances privacy while allowing controlled personalization based on explicit user intent.
3Loss of information
If third-party trackers are allowed to follow users across websites, then data acquisition for personalization is improved, but user experience degradation increases
Solution Approach 1:
The patent segments tracking requests into first-party (same domain) and third-party (external domain) sub-requests. It selectively blocks third-party trackers that cause cross-site tracking and user experience degradation, while permitting first-party tracking that supports legitimate personalization and site functionality, thus maintaining ease of operation.
Data Source
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AI summary
A network apparatus detects connection requests and extracts related data. The data is analyzed to determine whether the host is in an active state, whether the host matches a domain referrer and an amount of time from a last connection request. If it is detected that the host is not in an active state, the host is not matching the domain referrer and the amount of time from the last connection request exceeds a predetermined new session threshold, then a connection request is classified as a main request. If the amount of time from the last connection request is below a predetermined continuous session threshold, then any connection requests following the main request are classified as sub-requests. If the domain of host in the active state does not match current host for a sub-request, the sub-request is classified as a third-party request.