Year: 2025
Type: article
Abstract: Watermarking is an effective technique to prevent illegal copying of datasets in data markets. However, existing techniques on tabular datasets either fall short in the three fundamental goals (detectability, non-intrusiveness, and robustness) or lack the capabilities of being blind and buyer-traceable. In this paper, we propose a blind and buyer-traceable watermarking scheme, B 2 Mark, based on statistical hypothesis testing. To our best knowledge, this is the first blind watermarking scheme applicable to both numerical and categorical data. During the embedding process, B 2 Mark embeds multi-bit watermark information as a buyer identifier based on the value domain (instead of noise domain) partition and a cryptographic hash function. In the detection phase, B 2 Mark employs a hypothesis testing-based approach to extract the embedded buyer identifier without accessing the original dataset, ensuring buyer-traceable and blind watermark detection. Experiments on various datasets confirm that B 2 Mark achieves three fundamental goals and can effectively and efficiently trace data leaks in multi-buyer scenarios.
DOI: 10.1145/3749158
Authors: Yihao Zheng, Jinfei Liu, Kui Ren, Li Xiong
Institutions: Zhejiang University, Emory University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7082999022