App Permission Privacy Scoring via NLP Deviation Analysis
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Solution Overview
Problem
Current privacy tools struggle to accurately assess app permissions due to their reliance on weighted scores and manual categorization, which can be subjective, time-consuming, and language-dependent, failing to account for novel features and potentially unwanted privacy implications.
Innovation Solution
A framework that quantifies deviations in app permissions using application descriptions by identifying a subset of words, determining expected permissions, comparing required permissions, and assigning a privacy score, enabling informed decision-making about app installations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization and weighted scores are used to assess app permissions, then the assessment process can be implemented with simple tools, but the accuracy and objectivity of the privacy assessment deteriorates due to subjectivity and language barriers
Solution Approach 1:
The patent replaces manual categorization and weighted scoring mechanisms with an automated natural language processing system. The system uses computational algorithms to analyze app descriptions, extract features, and generate privacy scores automatically, eliminating human subjectivity and language barriers while improving assessment accuracy.
Solution Approach 2:
The patent introduces an intermediary NLP-based analysis layer between the app description and the privacy assessment. This intermediary system processes the app description text, extracts relevant features, and transforms them into structured data that can be objectively evaluated, thereby improving measurement precision without requiring complex manual intervention.
2Productivity
If manual categorization of apps is performed to assess permissions, then the assessment can be based on app functionality, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent enables the system to perform self-service by automatically analyzing app descriptions using NLP techniques. The system extracts features, categorizes apps, and generates privacy scores without requiring manual intervention, thereby dramatically improving productivity and eliminating time losses associated with manual categorization.
Solution Approach 2:
The patent substitutes manual categorization processes with automated computational algorithms. The NLP system processes app descriptions, identifies key features, and performs classification automatically, replacing the time-consuming manual workflow with a rapid automated system that maintains assessment quality while improving productivity.
3Adaptability or versatility
If weighted scores for permissions are used, then the assessment can be simplified, but the system fails to account for novel features and app-specific contexts
Solution Approach 1:
The patent implements a dynamic assessment framework where the system adapts to novel features by continuously analyzing app descriptions through NLP. The feature extraction process identifies new and emerging app functionalities, allowing the system to adjust its assessment criteria dynamically rather than relying on static weighted scores, thereby improving adaptability while managing complexity through automated processing.
Data Source
AI summary
Methods, systems, and media for determining application permissions are provided. In some embodiments, the method comprises: receiving a description of an application to be installed on a user device and a group of permissions required by the application; identifying a subset of words in the description of the application; determining an expected group of permissions based on the subset of words; comparing the group of permissions required by the application and the expected group of permissions; determining a privacy score associated with the application based on the comparison of the group of permissions required by the application and the expected group of permissions; and causing the application to be installed on the user device based on the privacy score associated with the application.