Fake Personal Data Policy for Mobile Device Security
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Solution Overview
Problem
Conventional data security systems are not suitable for mobile communications devices, as they are designed for corporate or enterprise-level data protection rather than personal data management on mobile devices.
Innovation Solution
A method is introduced to identify personal data on a mobile communications device, generate a policy based on this data, and monitor access to prevent unauthorized access or leakage of personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data security systems are used on mobile communications devices, then corporate data protection is improved, but personal data management suitability deteriorates
Solution Approach 1:
The patent creates personalized policies tailored to each user's specific personal data and usage patterns rather than applying uniform corporate security measures. The system analyzes individual user behavior and generates customized access control policies that are locally optimized for personal data protection on mobile devices.
Solution Approach 2:
The system dynamically adjusts security parameters based on user behavior analysis and policy requirements. It transforms static corporate security configurations into adaptive personal data protection mechanisms that modify access controls, monitoring levels, and security measures according to individual user patterns and contexts.
2Reliability
If personalized policies are created based on personal data, then personal data protection is improved, but system complexity increases
Solution Approach 1:
The system automatically analyzes user personal data, identifies sensitive information, and generates personalized security policies without requiring manual user configuration. The mobile device and server work together to self-configure protection mechanisms based on observed usage patterns and data classification.
Solution Approach 2:
The system performs preliminary analysis of personal data and user behavior patterns before actual data access occurs. By pre-establishing policies based on anticipated access scenarios and classified data sensitivity, the system prepares protection measures in advance, reducing the complexity of real-time decision-making.
3Reliability
If access monitoring is implemented to detect unauthorized access, then data security is improved, but processing overhead increases
Solution Approach 1:
The system implements selective monitoring that focuses only on access attempts to classified personal data rather than monitoring all data access uniformly. By applying monitoring only where necessary based on policy-defined sensitivity levels and user patterns, the system reduces overall processing overhead while maintaining security for critical data.
Solution Approach 2:
The system continuously monitors access patterns and provides feedback to refine policy enforcement and detection mechanisms. By learning from actual access behavior and adjusting monitoring intensity accordingly, the system optimizes the balance between security detection and processing resource consumption over time.
Data Source
AI summary
Systems and methods are described for managing personal data on a client computer, in which personal data associated with the client computer is identified by a policy management module. An analysis may be performed for the client computer by a server using fake personal data. To identify applications which may attempt to access the personal data, the fake personal data is tracked for access on the client computer. A policy is then created that disallows access of the personal data by any applications identified by the analysis of the client computer as accessing the fake personal data. The policy is then assigned to the client computer.