Mobile Message Masking via Sensory Format Transformation
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Solution Overview
Problem
Existing methods for masking mobile message content, such as text or audio, are inadequate due to the lack of redundancy in text data and sensitivity concerns, particularly in situations where privacy is compromised.
Innovation Solution
A computer-implemented method that categorizes and masks messages using speech recognition and machine learning to transform them into vibration, sound, or graphic formats, ensuring only the intended recipient can decipher the content, with the ability to automatically generate categories and adjust masking based on user and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If text steganography is used to mask messages, then message privacy is improved, but the complexity of the system increases due to lack of redundancy in text data
Solution Approach 1:
The system changes the representation parameters of text data by transforming it into different formats (vibration patterns, sound waves, graphic images) based on message category. This parameter transformation enables steganography without requiring complex text-based hidden message structures, thus improving privacy while reducing system complexity.
Solution Approach 2:
The patent replaces text-based steganography mechanisms with sensory-based mechanisms (vibration, sound, visual graphics). Instead of hiding messages within text characters, the system uses physical sensory outputs that can be perceived but not easily analyzed, substituting a simpler mechanism for the complex text processing required in traditional steganography.
2Quantity of substance
If existing data compression approaches like Huffman coding are used, then data size is reduced, but the ability to mask information based on sensitivity is lost
Solution Approach 1:
The system segments the message processing into distinct stages: message reception, category determination, and format transformation. By segmenting the process, the system can apply compression techniques during category determination while preserving the masking capability in the transformation stage, thus achieving both data size reduction and information masking based on sensitivity.
Solution Approach 2:
The system dynamically adjusts the masking format based on message category and sensitivity level. Different message categories are transformed into different sensory formats (vibration, sound, graphics), allowing the system to adapt the masking strategy to the specific information being transmitted, thereby maintaining masking capability while managing data size.
3Object-affected harmful factors
If messages are transformed into sensory formats for masking, then privacy in sensitive environments is improved, but the device complexity increases due to multiple output channels
Solution Approach 1:
The system implements a universal message masking framework that can output multiple sensory formats (vibration, sound, graphics) through a single integrated processing pipeline. The same core system handles all message types and privacy scenarios, making the device multi-functional without requiring separate complex systems for each output channel, thus reducing overall device complexity.
Solution Approach 2:
The system uses parameter changes in the transformation process to adapt messages to different sensory formats based on message category and environmental conditions. By parameterizing the transformation rather than implementing separate processing paths for each format, the system manages complexity while providing multiple privacy-protecting output channels.
Data Source
AI summary
A method, an apparatus and an article of manufacture for masking a message on an electronic device. The method includes receiving a message on an electronic device, determining if a message category label is included in the message, mapping the message category to a corresponding masking format if a message category label is included in the message, extracting the content of the message to generate a message category if a message category label is not included in the message, wherein each message category generated corresponds to a masking format, and masking the message on the electronic device by transforming the message into the masking format that corresponds to the message category for the message.