Cookie Classification via Flow Pattern Analysis
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Solution Overview
Problem
Current methods for protecting user privacy on the Internet, such as blacklists, are inadequate in identifying and mitigating cookies that track users without their knowledge, leading to compliance issues with regulations like GDPR, as they do not consider diverse factors in cookie generation and sharing flows.
Innovation Solution
A computer-implemented method that automatically classifies cookies by analyzing their generation and sharing flows, determining flow patterns, and creating a stratified classification to identify suspicious cookies, which can then trigger security actions like blocking or alerting administrators, thereby enhancing privacy protection and compliance with regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If blacklist methods are used to protect user privacy, then implementation simplicity is improved, but detection precision deteriorates because blacklists can only identify malware using existing database of file hash functions
Solution Approach 1:
The patent segments the cookie analysis process into multiple independent components: cookie generation flow analysis, cookie sharing flow analysis, feature extraction modules, and pattern determination modules. Each component handles a specific aspect of cookie behavior, allowing the system to achieve high detection precision through comprehensive analysis while maintaining implementation simplicity through modular design.
Solution Approach 2:
The patent transitions from traditional one-dimensional blacklist matching to multi-dimensional analysis by examining cookies across multiple dimensions: generation flows, sharing flows, behavioral patterns, and contextual relationships. This dimensional expansion enables the system to detect suspicious cookies that would be invisible to simple blacklist methods.
2Measurement precision
If comprehensive analysis of cookie generation and sharing flows is performed, then detection precision is improved, but device complexity increases
Solution Approach 1:
The system divides the complex analysis task into separate modular components: a cookie generation flow analyzer, a cookie sharing flow analyzer, feature extraction modules, and pattern determination modules. Each module performs a specific function and can be independently implemented, maintained, and optimized, reducing overall system complexity while achieving comprehensive detection precision.
Solution Approach 2:
The patent introduces intermediary elements such as feature extraction layers and pattern determination modules that mediate between raw cookie data and final detection results. These intermediaries simplify the analysis process by transforming complex raw data into structured features and identifiable patterns, making the overall system more manageable despite its comprehensive nature.
3Productivity
If automatic classification with stratified analysis is implemented, then productivity is improved by reducing manual analysis, but device complexity increases due to automated analysis systems
Solution Approach 1:
The system implements self-service automation where the cookie analysis system automatically performs classification, pattern recognition, and suspicious cookie identification without requiring manual intervention. The automated classification system processes cookies through stratified analysis layers, extracting features and determining patterns autonomously, which significantly improves productivity while the modular architecture keeps complexity manageable.
4Measurement precision
If flow pattern analysis is performed on cookie generation and sharing, then detection precision is improved, but loss of time increases due to comprehensive feature extraction
Solution Approach 1:
The patent performs preliminary action by pre-defining feature extraction templates and pattern recognition rules before actual cookie analysis begins. Common cookie behaviors and suspicious patterns are pre-characterized, allowing the system to quickly match observed cookies against these pre-established criteria during runtime, thereby maintaining high detection precision while minimizing analysis time.
Solution Approach 2:
The system applies partial action by focusing feature extraction and pattern analysis only on the most relevant and discriminating features of cookie flows, rather than analyzing every possible attribute in equal detail. This selective approach maintains high detection precision by concentrating computational resources on the most informative aspects of cookie behavior.
Data Source
AI summary
Analyzing and mitigating website privacy issues by automatically classifying cookies. In some embodiments, a method may include identifying a cookie for a website, obtaining a cookie generation flow related to generation of the cookie, obtaining a cookie sharing flow related to sharing of the cookie, extracting features related to the cookie generation flow and the cookie sharing flow, determining flow patterns in the cookie generation flow and the cookie sharing flow, automatically creating a stratified classification for the cookie based on the features and the flow patterns that explains how the cookie interacts with a website and with third-party resources loaded on the website, determining whether the stratified classification for the cookie exceeds a weighted suspiciousness threshold value, and in response to determining that the stratified classification for the cookie exceeds the weighted suspiciousness threshold value, determining that the cookie is suspicious and initiating a security action on the cookie.