# A Comprehensive Survey of Watermarking Relational Databases Research

arXiv: https://arxiv.org/abs/1801.08271 · HTML: https://mlsanity.com/paper/1801.08271/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks](https://mlsanity.com/paper/2304.11285/) (2023)
- [TabularMark: Watermarking Tabular Datasets for Machine Learning](https://mlsanity.com/paper/2406.14841/) (2024)
- [RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2501.05249/) (2025)
- [Adaptive and Robust Watermark for Generative Tabular Data](https://mlsanity.com/paper/2409.14700/) (2024)
- [RaMark: Radioactive Watermarking for Generated Tabular Data](https://mlsanity.com/paper/2607.09000/) (2026)

## 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.