Privacy Policy Configuration via Latent User Intent Classification
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Solution Overview
Problem
Existing mobile platforms lack effective means to distinguish between legitimate and illegitimate usage of private information by applications, leading to privacy threats and inefficiencies in real-time monitoring, with current taint analysis and statistical approaches failing to provide customized and adaptable solutions for users.
Innovation Solution
A method that configures a privacy policy by identifying installed applications, formulating privacy-related questions, receiving user responses, determining classified privacy instances, and establishing a latent, implicit privacy policy using machine learning classification, specifically through K-star clustering-based classification and natural distance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If taint analysis is used to track private information flow, then privacy monitoring capability is improved, but false alarm rate increases and measurement precision deteriorates
Solution Approach 1:
The patent changes the parameter of information flow tracking from simple Boolean reachability queries to quantitative analysis using entropy and information flow metrics. This allows the system to measure not just whether data flows from source to sink, but the actual amount and sensitivity of information transmitted, thereby reducing false alarms while maintaining privacy monitoring capability
Solution Approach 2:
The patent introduces an intermediary classification layer between raw taint analysis results and final privacy violation detection. This intermediary uses machine learning classifiers to evaluate whether detected data flows constitute actual privacy violations, filtering out false positives while maintaining sensitivity to real threats
2Ease of operation
If permission models are used to control application access, then ease of operation is improved, but adaptability deteriorates as platforms cannot distinguish legitimate from illegitimate usage
Solution Approach 1:
The patent transforms the static permission model into a dynamic system that continuously monitors and adapts to application behavior. Instead of granting blanket permissions, the system dynamically evaluates each data access request based on learned patterns of legitimate versus illegitimate usage, enabling both ease of operation and adaptability
Solution Approach 2:
The patent implements feedback mechanisms where the system learns from user responses to privacy questions and from classified privacy instances. This feedback loop enables the platform to adapt its permission decisions over time, distinguishing legitimate from illegitimate usage while maintaining user-friendly operation
3Measurement precision
If quantitative information flow tracking is implemented, then measurement precision is improved, but device complexity and productivity worsen due to high computational requirements
Solution Approach 1:
The patent applies partial action by selectively performing quantitative information flow tracking only for data flows that pass initial filtering thresholds. Instead of analyzing all possible data paths, the system focuses computational resources on suspicious or high-risk flows, reducing overall complexity while maintaining measurement precision where needed
Solution Approach 2:
The patent segments the information flow analysis into multiple stages: initial Boolean reachability filtering, followed by quantitative entropy analysis for suspicious paths, and finally machine learning classification. This segmentation reduces device complexity by breaking down the computationally intensive task into manageable stages that can be executed efficiently
Data Source
AI summary
Configuring a privacy policy by identifying one or more applications that are installed on a mobile device. A set of privacy-related questions are formulated based upon the one or more identified applications. A response to the set of privacy-related questions is received from a user. One or more classified privacy instances are determined for the user from the received response. A latent, implicit privacy policy is established from the one or more classified privacy instances.