Multi-Layer Guilt Assignment for Leaked Data Identification
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Solution Overview
Problem
Current solutions fail to effectively verify ownership and identify unauthorized use of textual and database data, which often leaks into unauthorized hands, lacking the ability to track distribution history and assign guilt accurately.
Innovation Solution
A multi-layered guilt assignment model and scoring method that utilizes a reference database of historical attributes to identify the original recipient and date of distribution, employing techniques like watermarking, fingerprinting, and statistical analysis to assign a cumulative guilt score to potential bad actors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional watermarking solutions are used for graphical, video, audio, or document data, then ownership verification is improved, but text and database file leakage remains unsolved
Solution Approach 1:
The patent applies fingerprinting techniques universally across multiple data types including text files, database files, spreadsheets, and documents, making the solution adaptable to various formats where traditional watermarking failed. The system processes different file types through unified fingerprint extraction and comparison mechanisms.
Solution Approach 2:
The patent replaces traditional watermarking mechanisms with fingerprinting-based detection systems. Instead of embedding visible or hidden watermarks in media files, the system extracts unique fingerprints from text and database structures, enabling ownership verification through pattern recognition rather than mechanical watermark embedding.
2Adaptability or versatility
If data is transmitted to multiple Trusted Third Parties for processing, then data utility is improved, but tracking the original recipient and assigning guilt becomes difficult
Solution Approach 1:
The patent embeds fingerprints in data before distribution to multiple Trusted Third Parties. These fingerprints serve as pre-prepared tracking markers that enable later identification of the original recipient even after multiple layers of distribution. The fingerprinting occurs in advance, allowing the system to trace leaked data back to its source.
Solution Approach 2:
The system implements feedback mechanisms where fingerprint matching results provide information about data distribution paths. By analyzing which fingerprints match leaked data, the system receives feedback about the leakage source and can refine its tracking accuracy across multiple distribution layers.
3Measurement precision
If comprehensive data tracking and guilt assignment systems are implemented, then leakage source identification is improved, but system complexity increases
Solution Approach 1:
The patent extracts essential identifying features (fingerprints) from data files and separates them from the main data content. This extraction allows the complex tracking and guilt assignment functionality to operate on compact fingerprint representations rather than entire data files, reducing computational complexity while maintaining identification accuracy.
Data Source
AI summary
A system and method for identifying a leaked data file and assigning guilt to one or more suspected leakers proceeds through a plurality of levels. At a first level, primary watermark detection occurs. Data is inserted into a subset of data to determine correlation with data in the suspected leaked file. The guilt probability that results is then weighted based on the number of bits matched. In a second level, another search process is performed for detecting additional salt-related patterns. The guilt score is then computed for every detected recipient identifier for the suspected leaked data file, and the relative guilt of these recipients is weighted. In a third layer, the statistical distribution of data in the suspected leaked file is compared with that of corresponding data in the reference files. After this layer is complete, the average of guilt scores across each of the layers is calculated.