Browser Cookie Control via Machine Learning Probability
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Solution Overview
Problem
Existing technologies struggle to effectively control and manage third-party cookies in web browsers, which can compromise user privacy and lead to targeted advertising based on browsing behavior.
Innovation Solution
A method and system that utilize a machine-learning algorithm (MLA) to determine the likelihood of a user accepting or declining third-party cookies based on past interactions with third-party web resources, providing personalized notifications to users and controlling cookie sharing accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If third-party cookies are transmitted to track web-browsing activity, then web resources can monitor user behavior and provide targeted advertising, but user privacy is compromised and online security is weakened
Solution Approach 1:
The patent introduces an intermediary component (browser extension or privacy protection module) that sits between third-party web resources and the user's browsing data. This intermediary intercepts cookie transmission requests, evaluates them against privacy policies, and selectively blocks or allows cookies based on domain reputation, user preferences, and security criteria, thereby protecting user privacy while maintaining legitimate tracking functionality
Solution Approach 2:
The patent implements differential privacy protection where different levels of cookie blocking are applied to different third-party domains based on their reputation and trustworthiness. High-trust domains (e.g., major social media platforms) may have cookies allowed while low-trust or suspicious domains are blocked, creating localized privacy protection tailored to each third-party resource rather than a blanket approach
2Object-affected harmful factors
If third-party cookies are blocked to protect user privacy, then online privacy is improved, but web resources lose the ability to track user behavior and provide personalized services
Solution Approach 1:
The patent segments third-party cookies into different categories (essential, analytical, promotional, etc.) and applies different blocking rules to each category. Essential cookies that enable basic functionality are allowed, while promotional and overly invasive tracking cookies are blocked. This segmentation enables personalized services to function through essential cookies while blocking privacy-invasive tracking
Solution Approach 2:
The patent implements a feedback mechanism where the browser monitors user interactions with blocked cookies and adjusts blocking decisions over time. If users consistently allow cookies from certain domains, the system learns from this feedback and becomes less restrictive toward those domains, thereby maintaining personalized service capability while protecting privacy through adaptive decision-making
3Ease of operation
If machine-learning algorithm is used to determine cookie acceptance likelihood, then personalized privacy control is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine-learning model offline using historical browsing data and cookie interaction patterns. The model is trained beforehand to recognize patterns of user cookie acceptance/declination behavior, and this pre-trained model is then deployed to the browser where it makes rapid predictions without requiring complex real-time computations, thereby reducing operational complexity while maintaining personalized control
Solution Approach 2:
The patent simplifies the machine-learning implementation by using lightweight models with fewer parameters and simpler architectures (such as decision trees or simple neural networks) rather than complex deep learning models. The system adjusts model parameters and complexity based on available computational resources, using more sophisticated models only when sufficient resources are available, thereby balancing personalized control capability with device complexity constraints
Data Source
AI summary
A method and a system for controlling tracking of web-browsing activities of a user in a browser application are provided. The method comprises: receiving, from a given web server, data representative of a web page to be displayed in the browser application; identifying, based on the data, elements of the web page linked to at least one in-use third-party web resource; obtaining in-use data including at least data of past user interactions of the user with the at least one in-use third-party web resource; feeding the in-use data to an MLA to determine a probability value of the user allowing sharing a respective third-party cookie of the at least one in-use third-party web resource therewith while browsing the web page; in response to the probability value being lower than a threshold value, determining that the user is unlikely to allow sharing the respective third-party cookie while browsing the web page.