Local-Language Passphrase Generation for Recall and High Entropy
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Solution Overview
Problem
Basic Emergent Users (BEUs) face challenges in generating high entropy passphrases due to limited vocabulary and language barriers, making them vulnerable to dictionary attacks and social circle threats, with existing systems failing to provide robust passphrase generation tailored to their needs.
Innovation Solution
A method and system that utilizes Automated Speech Recognition (ASR) to convert spoken phrases into text, filters Seed List Words (SLWs) based on a personalized intersection corpus, generates a passphrase distance matrix, and displays high entropy passphrases at optimal screen positions for easy recall, ensuring security and usability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If BEUs use simple passphrases from limited vocabulary, then recallability is improved, but security entropy deteriorates
Solution Approach 1:
The patent introduces an Automated Speech Recognition (ASR) engine as an intermediary that converts the user's spoken local language phrase into text. This mediator bridges the gap between the user's limited vocabulary and the need for high-entropy passphrases by accurately transcribing speech into secure passphrase candidates while preserving the user's natural language expression.
Solution Approach 2:
The system transforms the passphrase generation parameters by converting from text input to speech input, and then processing through multiple stages (speech-to-text conversion, seed list word filtering, distance matrix generation). This parameter transformation enables BEUs to generate high-entropy passphrases in their local language while maintaining recallability through the speech-based interface.
2Ease of operation
If passphrases are generated in local language, then usability for BEUs is improved, but vulnerability to dictionary attacks increases
Solution Approach 1:
The patent adds a new dimension to passphrase security by incorporating speech-based input and local language processing. Instead of relying solely on text-based dictionaries, the system uses speech recognition and vector distance calculations to create passphrases that exist in a different dimensional space, making traditional dictionary attacks less effective while improving usability for local language speakers.
Solution Approach 2:
The system performs preliminary filtering of seed list words and generates distance matrices before final passphrase selection. This preliminary action pre-processes the local language vocabulary to identify and exclude commonly attacked words, preparing a filtered set of high-entropy candidates that maintain usability while reducing dictionary attack vulnerability.
3Reliability
If ASR engine processes more words, then passphrase entropy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the passphrase generation process into distinct stages: speech-to-text conversion, seed list word filtering, distance matrix generation, and final passphrase selection. By dividing the complex processing into manageable segments, the system can handle larger vocabularies and generate higher entropy passphrases while keeping each processing stage's complexity manageable and optimized.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables BEUs to generate and select high entropy passphrases that are secure yet easy to remember, enhancing device authentication robustness against attacks.
Implementation Method 1
prompting a user, to speak out a recallable phrase comprising a set of words using the natural language of the user, wherein the recallable phrase is converted to a text using an Automated Speech Recognition (ASR) engine
Data Source
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AI summary
There are technical challenges to address the technical capability limitations of a Basic Emergent User (BEU) to enable and assist in generating high entropy passphrases but are easier for recall. Generation usable high entropy passphrases in local language from personalized intersection corpus for device authentication set up for BEU is provided. A recallable phrase, spoken by the BEU in local language, is converted to a text and Seed List Words (SLWs) are filtered based on a pre-generated personalized intersection corpus. Passphrase distance matrix is generated for the SLWs using the personalized intersection corpus. Words associated with each SLW are arranged in descending order of vector distance or entropy. Words of same order are concatenated in for each SLW to generate passphrase corpus. Randomly selected passphrases are read out and displayed on the user device by positioning the highest entropy based on usability of display screen in context of the user.