Crowd Sourced File Recognition via Hashing
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Solution Overview
Problem
Users face difficulties in locating specific content within large libraries of encrypted digital files from various sources, as there is no standard for identifying content within protected files, making it burdensome to find desired content without decrypting the files.
Innovation Solution
A method using crowd sourcing and one-way hashing algorithms to identify protected files by processing available file attributes, generating identifiers, and querying databases to determine content titles, allowing users to select the correct title from presented options, which are then recorded and weighted for future identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If files are encrypted for security purposes, then security protection is improved, but file content identification becomes difficult
Solution Approach 1:
The system performs preliminary actions by extracting and hashing file attributes (metadata, filename, size, date) before encryption is applied, creating a fingerprint that identifies the file content without requiring decryption. This preliminary identification mechanism allows files to be recognized and organized even in encrypted state.
Solution Approach 2:
A hashing algorithm acts as an intermediary between the encrypted file and the identification system. The hash function processes file attributes to generate a unique identifier that bridges the gap between encrypted content and searchable metadata, enabling identification without exposing the actual encrypted data.
2Adaptability or versatility
If files are obtained from various different sources, then file collection diversity is improved, but content identification becomes more difficult
Solution Approach 1:
The system implements a universal identification mechanism that works across files from any source. By using a standardized hashing approach on common file attributes (metadata, filename, size, modification date), the system creates a source-agnostic identification method that adapts to diverse file collections regardless of origin or encryption scheme.
Solution Approach 2:
The system transforms various file attributes (metadata, filename, size, date) into a standardized parameter format through hashing. This parameter transformation converts diverse file characteristics from different sources into a uniform identifier structure that can be consistently processed and compared across the entire file collection.
3Device complexity
If traditional file identification methods are used, then simplicity is maintained, but identification accuracy decreases for encrypted files
Solution Approach 1:
The system replaces traditional mechanical file identification methods (which require opening and decrypting files) with a computational hashing approach. Instead of physically accessing file content through decryption, the system uses algorithmic processing of file attributes to generate identifiers, substituting a complex mechanical process with a simpler computational one that achieves the same identification goal.
Solution Approach 2:
The system creates a digital copy or representation of file identity through hashing. Rather than working with the actual encrypted file content, the system generates a hash copy that captures the essential identifying characteristics of the file. This hash copy serves as a surrogate that enables identification without requiring access to the original encrypted content.
Data Source
AI summary
Methods for identifying encrypted content in ones of a plurality of encrypted data files in a library of encrypted files without decrypting the data files utilize crowd sourcing for content identification. A method includes selecting, by a computer, content titles for presenting with ones of identifiers for the data files in a data structure. Each of the identifiers includes a hash of metadata for one of the data files and the content titles include a character string that identifies each file's content. The user selection data identifies the content titles that correspond to the data files. The computer determines which content titles satisfy a minimum confidence threshold for associating with one of the identifiers, based on a quality or quantity of the multiple independent clients supplying the user selection data. An apparatus for performing the method includes a memory holding instructions for performing steps of the method as summarized above.