# Adversarial Contract Design for Private Data Commercialization

arXiv: https://arxiv.org/abs/1810.07608 · HTML: https://mlsanity.com/paper/1810.07608/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [FL-Market: Trading Private Models in Federated Learning](https://mlsanity.com/paper/2106.04384/) (2021)
- [A Survey of Data Pricing for Data Marketplaces](https://mlsanity.com/paper/2303.04810/) (2023)
- [Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy](https://mlsanity.com/paper/2505.05707/) (2025)

## Abstract (arXiv)

The proliferation of data collection and machine learning techniques has created an opportunity for commercialization of private data by data aggregators. In this paper, we study this data monetization problem using a contract-theoretic approach. Our proposed adversarial contract design framework accounts for the heterogeneity in honest buyers' demands for data, as well as the presence of adversarial buyers who may purchase data to compromise its privacy. We propose the notion of Price of Adversary (PoAdv) to quantify the effects of adversarial users on the data seller's revenue, and provide bounds on the PoAdv for various classes of adversary utility. We also provide a fast approximate technique to compute contracts in the presence of adversaries.