Personal Identity Analyzer for Sensitive Document Protection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Personal computing systems face challenges in identifying and protecting sensitive documents containing personal information, which can be misused if stolen, due to the vast number of documents and types of data stored, making it difficult to analyze and secure such information effectively.
Innovation Solution
A system and method that utilize a personal identity analyzer to obtain and analyze personal identity information from various sources, including the operating system, email clients, and web browsers, to identify sensitive documents and optionally protect them by restricting access and encrypting the documents, ensuring they are not misused or stolen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive document analysis is performed to identify all sensitive documents, then identification completeness is improved, but system complexity and processing time increase significantly
Solution Approach 1:
The system segments the document analysis process into multiple independent modules: file type detection module, pattern matching module, machine learning analysis module, and encryption detection module. Each module handles specific aspects of sensitive information detection, allowing parallel processing and reducing overall system complexity while maintaining comprehensive identification capability.
Solution Approach 2:
The patent introduces an intermediary layer between the raw documents and the analysis engine, including pre-processing components that normalize document formats, extract metadata, and prepare data structures. This intermediary layer simplifies the core analysis logic and improves processing efficiency without compromising identification completeness.
2Measurement precision
If comprehensive document analysis is performed to identify all sensitive documents, then identification completeness is improved, but processing time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-compiling lists of sensitive patterns, pre-training machine learning models, and pre-establishing encryption detection rules before actual document analysis. During runtime, these pre-prepared resources enable rapid matching and analysis, significantly reducing processing time while maintaining high identification completeness.
Solution Approach 2:
The patent implements periodic action through incremental scanning and batch processing mechanisms. Instead of analyzing all documents simultaneously, the system processes documents in manageable batches with periodic updates to detection rules and models, improving throughput and reducing overall processing time while ensuring comprehensive coverage.
3Reliability
If encryption detection is performed to identify protected documents, then security protection is improved, but false positive rate increases
Solution Approach 1:
The system implements feedback mechanisms where detection results are continuously evaluated and refined. When encryption patterns are detected, the system cross-validates with multiple detection methods, analyzes contextual information, and adjusts detection thresholds based on historical data. This feedback loop reduces false positives while maintaining high security protection by confirming genuine encrypted documents.
Data Source
AI summary
Systems and methods obtain personal identity information, identify a user's personal documents containing sensitive information, and can optionally protect the sensitive documents. A user's personal identity information can be obtained from various sources such as operating system, email clients, web browsers, Active Directory or from user's documents. The user's documents on hard drives, cloud storage etc. can be searched. Sensitive documents with personal identities are identified and optionally protected against misuse and theft.