Fragment Hash Detection for Protected Data Classification
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Solution Overview
Problem
Current automated systems fail to detect fragments of protected data within documents, as they only compare hash values of entire files, missing instances where protected information is copied and integrated into new documents, leading to potential unauthorized disclosure.
Innovation Solution
A method and system that identify fragments of known protected data by comparing hashes of fragments from unknown files to a library of known files, using a sliding window algorithm to generate and compare hashes of fixed-length fragments, with optional preprocessing and hierarchical classification to determine the classification of the unknown file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hash comparison of entire files is used, then the system is simple to implement, but it fails to detect fragments of protected data within documents
Solution Approach 1:
The patent divides files into fixed-length fragments (e.g., 50-byte segments) and calculates hash values for each fragment individually. This segmentation allows the system to detect protected data even when it is embedded within larger documents, overcoming the limitation of whole-file hash comparison while maintaining a manageable processing approach through systematic division of the file into detectable units.
2Measurement precision
If fragment-based hash comparison is implemented, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent pre-calculates and stores hash values of all possible fixed-length fragments from known protected files in a database before actual detection occurs. During file inspection, the system only needs to calculate hashes of fragments from the unknown file and compare them against the pre-built database, significantly reducing processing time during actual detection operations while maintaining high accuracy.
3Measurement precision
If manual review of file contents is performed, then classification accuracy is high, but productivity is low
Solution Approach 1:
The patent implements an automated classification system that uses hierarchical classification rules and scoring mechanisms to automatically determine the classification level of protected data without requiring manual review. The system assigns scores based on matching fragment classifications and automatically categorizes files, achieving both high accuracy and high productivity by eliminating the need for human intervention in the classification process.
Data Source
AI summary
A system for identifying of presence of protected data in an unknown file includes a processor coupled to a memory storing instructions, the processor being configured to implement the instructions to apply a sliding window process to generate one or more fragments of length, for each generated fragment, check whether information about the generated fragment exists in a library of known fragments of protected data, and if the information about the generated fragment from the unknown file exists in the library of known fragments of protected data, perform steps to reflect an existence of the information about the generated fragment.