Personalized Filtering Profiles for Spam and Privacy Management
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Solution Overview
Problem
Current filtering systems in telecommunications struggle with personalized information management, as they often rely on operator-centric architectures, lack user preference feedback, and fail to effectively filter both spam and advertising messages, while also being costly and invasive regarding user privacy.
Innovation Solution
A global management system that employs dynamic, temporal, and contextual filtering profiles stored in individual and pooling databases, allowing users to define and update filtering rules based on their preferences, with community mechanisms that adapt to user behavior and preferences, and secure data exchange protocols to manage information classification and filtering across networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If centralized filtering systems are installed at the operator's network level, then information filtering capability is improved, but user trust deteriorates and response time to mass spam attack is relatively long
Solution Approach 1:
The system divides filtering authority into multiple segments: centralized filtering at operator level, distributed filtering at user device level, and community-based filtering through shared databases. This segmentation allows each layer to handle different aspects of filtering, improving both capability and user trust through transparency and user control.
Solution Approach 2:
The system implements feedback mechanisms where user preferences, spam reports, and filtering effectiveness data are continuously collected and used to update filtering rules in real-time. This feedback loop enables the system to adapt to new spam patterns while maintaining user trust through responsive and personalized filtering.
2Adaptability or versatility
If centralized filtering systems are installed at the operator's network level, then information filtering capability is improved, but response time to mass spam attack deteriorates
Solution Approach 1:
The system performs preliminary filtering actions by pre-loading spam signatures, maintaining updated filtering profiles in distributed databases at user devices, and pre-configuring filtering rules based on community feedback. When a mass spam attack occurs, the filtering is already in place, enabling immediate response without waiting for centralized system processing.
Solution Approach 2:
The system dynamically updates filtering rules and profiles in real-time based on emerging spam patterns, user feedback, and community-based filtering data. This dynamic adaptation allows the system to respond quickly to mass spam attacks by automatically adjusting filtering parameters without manual intervention or lengthy centralized processing.
3Adaptability or versatility
If local filtering systems are installed at the user's device level, then filtering personalization is improved, but system complexity deteriorates and bandwidth consumption increases
Solution Approach 1:
The system enables users to define and update their own filtering profiles through simplified interfaces, with automatic synchronization across devices and the centralized database. Users can easily express their preferences and report spam without dealing with complex configurations, while the system handles the complexity of profile management and distribution automatically.
4Adaptability or versatility
If local filtering systems are installed at the user's device level, then filtering personalization is improved, but operator network cost deteriorates
Solution Approach 1:
The system segments filtering operations between user devices and operator network. Personalized filtering rules are executed locally at user devices, reducing the need for operator network resources. Only essential updates to filtering profiles and aggregated spam data are transmitted through the operator network, significantly reducing bandwidth consumption and operational costs.
Data Source
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AI summary
The system has filtering softwares (L1, Ln) installed on access medium (M1, Mn) allowing users to define dynamic, temporal and contextual filtering profiles (P1, Pn). Each profile is formed of filters (Fi) defining an object of filtering characteristics e.g. modalities of filtering mechanisms, and emitter and receiver of information (I), where the modalities are positive filtering or negative filtering of information corresponding to utilization of one filter to find desired information or to process non desired information by deleting or quarantining the non desired information. An independent claim is also included for a method for assuring overall management of personalized filtering of information.