# FreqyWM: Frequency Watermarking for the New Data Economy

arXiv: https://arxiv.org/abs/2312.16547 · HTML: https://mlsanity.com/paper/2312.16547/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [FedPoP: Federated Learning Meets Proof of Participation](https://mlsanity.com/paper/2511.08207/) (2025)

## Abstract (arXiv)

We present a novel technique for modulating the appearance frequency of a few tokens within a dataset for encoding an invisible watermark that can be used to protect ownership rights upon data. We develop optimal as well as fast heuristic algorithms for creating and verifying such watermarks. We also demonstrate the robustness of our technique against various attacks and derive analytical bounds for the false positive probability of erroneously detecting a watermark on a dataset that does not carry it. Our technique is applicable to both single dimensional and multidimensional datasets, is independent of token type, allows for a fine control of the introduced distortion, and can be used in a variety of use cases that involve buying and selling data in contemporary data marketplaces.