Tabular Data Watermarking via Bin Replacement for Noise Robustness
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Solution Overview
Problem
Conventional watermarking of tabular data is not robust against noise and can be easily compromised by malicious actors, leading to potential disruption of trained models and data integrity issues.
Innovation Solution
A method involving binning and replacement of data elements within a range, using various schemes to create a robust watermarking process that includes tagging data elements, replacing values, and applying a watermark to generate a watermarked dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional watermarking is applied to tabular data, then data security is improved, but the watermark is not robust to noise and can be easily compromised
Solution Approach 1:
The patent divides the data range into multiple bins and applies different watermarking strategies to different bin types. First-type bins receive direct watermarking while second-type bins receive replacement watermarks, creating a segmented approach that distributes the watermark signal across multiple data regions, making it more robust to noise and local corruptions
Solution Approach 2:
The patent changes the parameter of data element values by replacing them with new values sampled from nearest bins of the first type. This parameter transformation embeds the watermark in a way that is resistant to noise, as the replacement values maintain statistical properties while carrying watermark information
2Reliability
If data elements are replaced to create robust watermarks, then watermark security is improved, but data fidelity may be compromised
Solution Approach 1:
The patent applies local quality by differentiating between first-type and second-type bins and applying different watermarking methods to each. The replacement operation is selectively applied only to second-type bins, while first-type bins retain their original values with direct watermarking, thus preserving data fidelity in critical regions while ensuring security in others
Solution Approach 2:
The patent uses copying by sampling new values from nearest bins of the first type to replace data elements in second-type bins. This copying approach preserves the statistical distribution and quality characteristics of the original data while embedding the watermark, as the sampled values replicate the local data patterns
3Reliability
If binning and replacement operations are applied, then noise resistance is improved, but processing complexity increases
Solution Approach 1:
The patent applies preliminary action by first binning the data range and classifying bins into first-type and second-type before applying the replacement operation. This preliminary organization of data into bins with distinct types enables efficient processing, as the algorithm only needs to iterate through and replace elements in second-type bins rather than processing all data uniformly
Data Source
AI summary
A method and system for watermarking a dataset generated by a source system are disclosed. The method includes acquiring the dataset, distributing data elements included in the dataset over a range, and dividing the range into multiple bins according to a scheme. The method further includes designating each of the bins as a first or second type, tagging each data element according to according to a bin type of a bin the respective data element falls into. For each data element included in a bin of the second type, selecting a new value by sampling within a nearest bin of the first type and replacing the respective data element with a replacement data element including the new value, and watermarking each of the data elements originally included in the bins of the first type and replacement data elements for generating a watermarked dataset.