Probabilistic Directory Entry Recovery in FAT File Systems
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Solution Overview
Problem
Current methods for recovering directory and file data in FAT file systems are inefficient, leading to a high number of false positives and missed critical data due to their basic approaches in searching for directory entries.
Innovation Solution
A probabilistic technique is employed to identify candidate blocks in a data store by analyzing characteristics such as reserved bytes, attribute bytes, temporal data, and file names, assigning initial probabilities, and increasing them for contiguous blocks, to accurately separate valid directory entries from random data, reducing false positives while ensuring a thorough search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive search of all unallocated areas is performed to find directory entries, then all critical data is found, but false positives increase significantly making analysis difficult
Solution Approach 1:
The patent applies parameter changes by analyzing multiple characteristics of candidate blocks including reserved byte values, attribute byte patterns, temporal data consistency, and file name formats. By evaluating these parameters collectively, the system distinguishes valid directory entries from random data with high accuracy, achieving both complete data recovery and precise identification with low false positive rates
Solution Approach 2:
The system uses feedback mechanisms by iteratively refining the identification process. Candidate blocks are initially identified, then evaluated against multiple characteristics, and results are used to adjust search parameters and focus subsequent analysis. This feedback loop continues until high-confidence directory entries are identified, ensuring both completeness and accuracy
2Measurement precision
If basic search methods are used to reduce false positives, then fewer false positives occur, but important data beyond algorithm scope is missed
Solution Approach 1:
The patent implements a universal identification approach that works across diverse data store conditions. The system evaluates multiple characteristics (reserved bytes, attribute bytes, temporal data, file names) that are applicable to various FAT file system versions and data corruption scenarios. This multi-functional approach ensures both high accuracy and complete recovery regardless of the specific data loss situation
Solution Approach 2:
The system transitions from single-criterion identification to multi-dimensional analysis by examining candidate blocks across multiple characteristics simultaneously. Instead of relying on one feature, the patent analyzes reserved byte patterns, attribute byte values, temporal data consistency, and file name formats together, creating a comprehensive identification framework that achieves both precision and completeness
3Measurement precision
If probabilistic technique with multiple characteristic testing is applied, then false positives are reduced, but computational complexity increases
Solution Approach 1:
The patent segments the identification process into distinct evaluation stages: initial candidate block identification, reserved byte testing, attribute byte analysis, temporal data verification, and file name validation. By dividing the complex probabilistic analysis into manageable segments, the system achieves high accuracy while maintaining algorithmic clarity and reducing apparent complexity
Solution Approach 2:
The system applies partial action by focusing computational resources on candidate blocks that meet initial criteria. Instead of performing full probabilistic analysis on every possible block, the patent first filters candidates using basic criteria, then applies comprehensive multi-characteristic testing only to promising candidates. This selective approach achieves high precision without excessive computational complexity
Data Source
AI summary
To recover deleted files in a data store, candidate blocks of the data store may be identified and, for each of the candidate blocks, a probability that the candidate block contains a directory entry may be determined. Directory entry metadata for candidate blocks that have a determined probability that is greater than a predetermined probability threshold may then be extracted.