Statistical Fingerprinting for Structured Dataset Ownership
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Solution Overview
Problem
Existing data watermarking and fingerprinting solutions are inadequate for large text-based datasets, particularly those with highly organized structures, such as databases and data feeds, where they fail to effectively establish ownership and prove data authenticity.
Innovation Solution
The implementation of statistical fingerprinting methods that characterize the internal structure of datasets using canonical statistical tests, generating unique fingerprints for identification and comparison, allowing for the determination of dataset identity and ownership.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing data watermarking and fingerprinting solutions are applied to large text-based datasets, then data ownership protection is provided, but the solutions are inadequate for highly organized structured data such as databases and data feeds
Solution Approach 1:
The patent transforms the fingerprinting approach by changing from content-based methods to statistical parameter-based methods. It extracts statistical parameters (mean, variance, skewness, kurtosis, etc.) from structured data subsets and uses these parameters as fingerprints. This parameter transformation enables effective fingerprinting of highly organized structured data like databases and data feeds that previous content-based methods could not handle adequately.
2Measurement precision
If statistical fingerprinting methods are used to characterize large structured datasets, then unique identification and ownership establishment is achieved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential statistical parameters from large structured datasets rather than processing the entire dataset. By taking out key statistical characteristics (mean, variance, skewness, kurtosis, and other distribution parameters) from each subset, it creates compact fingerprints that provide high identification accuracy while significantly reducing computational complexity compared to analyzing complete datasets.
Solution Approach 2:
The patent segments large structured datasets into smaller subsets (e.g., tables, columns, or data partitions) and computes statistical parameters for each subset independently. This segmentation approach enables efficient processing of large datasets by breaking them into manageable pieces, maintaining identification accuracy through comprehensive subset coverage while reducing overall computational burden.
3Reliability
If data alterations are made to create watermarks, then ownership evidence is provided, but data integrity and originality are compromised
Solution Approach 1:
The patent replaces the mechanical approach of data watermarking (which physically alters data by embedding hidden information) with a statistical analysis approach. Instead of modifying data to embed ownership information, it computes statistical parameters from the original data and uses these parameters as fingerprints. This substitution provides reliable ownership evidence while completely preserving data integrity and originality.
Data Source
AI summary
A system and method for statistical fingerprinting of structured datasets begins by dividing the structured database into groups of data subsets. These subsets are created based on the structure of the data; for example, data delineated by columns and rows may be broken into subsets by designating each column as a subset. A fingerprint is derived from each subset, and then the fingerprint for each subset is combined in order to create an overall fingerprint for the dataset. By applying this process to a “wild file” of unknown provenance, and comparing the result to a data owner's files, it may be determined if data in the wild file was wrongfully acquired from the data owner.