Spam Elimination via Information Density Compression
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Solution Overview
Problem
Traditional spam filtering methods in data streaming services are inefficient and prone to misidentifying useful messages as spam, due to their reliance on keyword recognition and combinatoric databases, leading to censorship and ineffective spam removal.
Innovation Solution
A method and system that utilize a lossless compression algorithm to identify spam messages by determining if they compress below a threshold level, distinguishing them from non-spam messages based on information density, and subsequently deleting or blocking them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spam filters use keyword recognition and combinatoric databases, then spam identification capability is improved, but false positive rate increases and useful messages are misidentified as spam
Solution Approach 1:
The patent changes the parameter used for spam identification from keyword presence to compression ratio. Instead of checking whether specific words appear in messages, the system compresses each message and measures the compression ratio. Spam messages, being low-information density, compress to a higher ratio than legitimate messages. This parameter change fundamentally resolves the contradiction by using a metric that naturally distinguishes spam without false positives.
2Adaptability or versatility
If vast databases of unwanted terms are maintained to defeat spam variations, then spam filtering coverage is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent extracts the essential characteristic of spam (low information density) and uses it directly for identification, rather than maintaining databases of unwanted terms. The compression algorithm automatically adapts to any spam variation because it measures information density rather than matching against predefined keywords. This extraction principle eliminates the need for vast databases while maintaining comprehensive spam filtering coverage.
3Productivity
If keyword-based spam filtering is used, then spam removal capability is improved, but data streaming efficiency decreases due to processing overhead
Solution Approach 1:
The patent replaces the mechanical system of keyword matching and database searching with a computational compression process. Lossless compression algorithms are highly optimized and can process messages rapidly without requiring complex pattern matching or database queries. The compression process inherently identifies spam through information density measurement, eliminating the time-consuming operations of traditional keyword-based filtering while maintaining or improving spam removal effectiveness.
Data Source
AI summary
A method for elimination of spam in a data stream according to information density, includes receiving, by a computing device, a stream of messages. The method includes directing, by the computing device, the stream into at least one buffer. The method includes repeatedly compressing, by the computing device, data in the buffer using a lossless compression algorithm. The method includes identifying, by the computing device, at least one first message in the buffer as spam, by determining that the at least one first message has been compressed below a threshold level.