# A Survey of Data Pricing for Data Marketplaces

arXiv: https://arxiv.org/abs/2303.04810 · HTML: https://mlsanity.com/paper/2303.04810/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 118 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [A Survey on Data Pricing: from Economics to Data Science](https://mlsanity.com/paper/2009.04462/) (2020)
- [Model-based Pricing for Machine Learning in a Data Marketplace](https://mlsanity.com/paper/1805.11450/) (2018)
- [Revenue Maximization for Query Pricing](https://mlsanity.com/paper/1909.00845/) (2019)
- [Redrawing the Boundaries on Purchasing Data from Privacy-Sensitive Individuals](https://mlsanity.com/paper/1401.4092/) (2014)
- [Buying Private Data without Verification](https://mlsanity.com/paper/1404.6003/) (2014)
- [The Economics of Social Data](https://mlsanity.com/paper/2004.03107/) (2020)
- [The Design of Arbitrage-Free Data Pricing Schemes](https://mlsanity.com/paper/1606.09376/) (2016)
- [Low-Cost Learning via Active Data Procurement](https://mlsanity.com/paper/1502.05774/) (2015)
- [Optimized, Direct Sale of Privacy in Personal-Data Marketplaces](https://mlsanity.com/paper/1701.00740/) (2017)
- [How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market](https://mlsanity.com/paper/1705.02982/) (2017)
- [Adversarial Contract Design for Private Data Commercialization](https://mlsanity.com/paper/1810.07608/) (2018)
- [Selling Data at an Auction under Privacy Constraints](https://mlsanity.com/paper/2005.09248/) (2020)
- [Money Walks: A Human-Centric Study on the Economics of Personal Mobile Data](https://mlsanity.com/paper/1407.0566/) (2014)
- [The Strange Case of Privacy in Equilibrium Models](https://mlsanity.com/paper/1508.03080/) (2015)

## Built on by (most cited first, 6 of 6)

- [From Points to Coalitions: Hierarchical Contrastive Shapley Values for Prioritizing Data Samples](https://mlsanity.com/paper/2512.19363/) (2025)
- [PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces](https://mlsanity.com/paper/2510.18109/) (2025)
- [Pricing Strategies for Different Accuracy Models from the Same Dataset Based on Generalized Hotelling's Law](https://mlsanity.com/paper/2404.05272/) (2024)
- [Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Services](https://mlsanity.com/paper/2503.23001/) (2025)
- [Trading Vector Data in Vector Databases](https://mlsanity.com/paper/2511.07139/) (2025)
- [Convex Dataset Valuation for Post-Training](https://mlsanity.com/paper/2605.16704/) (2026)

## Abstract (arXiv)

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This paper attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.