The Design of Arbitrage-Free Data Pricing Schemes
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Abstract (arXiv)
Motivated by a growing market that involves buying and selling data over the web, we study pricing schemes that assign value to queries issued over a database. Previous work studied pricing mechanisms that compute the price of a query by extending a data seller's explicit prices on certain queries, or investigated the properties that a pricing function should exhibit without detailing a generic construction. In this work, we present a formal framework for pricing queries over data that allows the construction of general families of pricing functions, with the main goal of avoiding arbitrage. We consider two types of pricing schemes: instance-independent schemes, where the price depends only on the structure of the query, and answer-dependent schemes, where the price also depends on the query output. Our main result is a complete characterization of the structure of pricing functions in both settings, by relating it to properties of a function over a lattice. We use our characterization, together with information-theoretic methods, to construct a variety of arbitrage-free pricing functions. Finally, we discuss various tradeoffs in the design space and present techniques for efficient computation of the proposed pricing functions.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 30 Jun 2016.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2.
4 papers.
- 2021Data Pricing in Machine Learning Pipelinescited by 17
- 2021FL-Market: Trading Private Models in Federated Learningcited by 9
- 2023A Survey of Data Pricing for Data Marketplacescited by 6
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- 2023Differentially Private Online Item Pricing19 May 2023
- 2023A Survey of Data Pricing for Data Marketplaces7 Mar 2023
- 2021Data Pricing in Machine Learning Pipelines18 Aug 2021
- 2021FL-Market: Trading Private Models in Federated Learning8 Jun 2021