A Comprehensive Survey of Watermarking Relational Databases Research
Not on the map. Why?
Abstract (arXiv)
Watermarking and fingerprinting of relational databases are quite proficient for ownership protection, tamper proofing, and proving data integrity. In past few years several such techniques have been proposed. A survey of almost all the work done, till date, in these fields has been presented in this paper. The techniques have been classified on the basis of how and where they embed the watermark. The analysis and comparison of these techniques on different merits has also been provided. In the end, this paper points out the direction of future research in these fields.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2023Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attackscited by 11
- 2024TabularMark: Watermarking Tabular Datasets for Machine Learningcited by 8
- 2025RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Modelscited by 6
- 2026RaMark: Radioactive Watermarking for Generated Tabular Datacited by 0
- 2026RaMark: Radioactive Watermarking for Generated Tabular Data10 Jul 2026
- 2025RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models9 Jan 2025
- 2024TabularMark: Watermarking Tabular Datasets for Machine Learning21 Jun 2024
- 2023Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks22 Apr 2023