Encryption Recommendation Device for Privacy Weighted Applications
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Solution Overview
Problem
Users face inefficiency in manually selecting private files and applications for encryption, especially as the number of items increases, leading to decreased selection efficiency.
Innovation Solution
A method and device that scan user operations, determine the frequency of usage of applications, and recommend frequently used applications with higher privacy weights for encryption, automatically suggesting files or applications to be encrypted based on preset thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually select private files or applications for encryption, then encryption security is ensured, but selection efficiency decreases as the quantity of files or applications increases
Solution Approach 1:
The system automatically analyzes user behavior patterns and performs encryption recommendations without requiring manual user selection. The terminal device scans user operations, determines frequency of usage, and autonomously identifies applications suitable for encryption, allowing the system to serve itself in the encryption decision-making process.
Solution Approach 2:
The system performs preliminary analysis of application usage patterns and privacy weights before the user needs to make encryption decisions. By pre-calculating frequency of usage and privacy weights based on scanned user operations, the system prepares encryption recommendations in advance, reducing the time required when users actually need to encrypt data.
2Measurement precision
If users manually select applications for encryption, then precise control over encrypted data is achieved, but time consumption increases
Solution Approach 1:
The system continuously scans user operations and uses the scanned data to dynamically adjust encryption recommendations. By implementing feedback loops where user behavior patterns are monitored and used to refine privacy weight calculations, the system improves selection precision over time while reducing manual intervention requirements.
Solution Approach 2:
The system changes parameters such as frequency of usage thresholds and privacy weight values based on analyzed user behavior. By adjusting these parameters dynamically according to scanned operations, the system optimizes the balance between selection precision and time consumption, automatically adapting to different user patterns without requiring manual reconfiguration.
3Measurement precision
If the system scans and analyzes all user operations to recommend encryption, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the analysis process into distinct modules: scanning user operations, determining frequency of usage, calculating privacy weights, and generating recommendations. By dividing the complex analysis task into separate functional units, the system improves recommendation accuracy through comprehensive analysis while managing complexity through modular architecture.
Solution Approach 2:
The system introduces intermediary data structures such as frequency counters and privacy weight calculations that mediate between raw user operations and final encryption recommendations. These intermediaries simplify the complex relationship between user behavior and encryption decisions, making the system more manageable while maintaining high recommendation accuracy.
Data Source
AI summary
An encryption recommendation method and an encryption recommendation device are provided. The method includes: scanning user operations on an application in a terminal, and obtaining a frequency of usage of each application; obtaining a set of frequently-used applications from the applications based on the frequency of usage of the each application; and determining, based on privacy weights of the set of frequently-used applications, at least one recommended application to be encrypted from the set of frequently-used applications.