A Marketplace for Data: An Algorithmic Solution
Name
1805.08125.pdf
Description
Accepted version
Size
1.31 MB
Format
Adobe PDF
Checksum (MD5)
8c7ed36eee8eb9cb5e5b0c24ffafe667
Author(s)
Agarwal, Anish
Dahleh, Munther A
Sarkar, Tuhin
Alternative Title
An Algorithmic Solution
Date Issued
June 2019
Journal
2019 ACM Conference on Economics and Computation
Publisher
Association for Computing Machinery (ACM)
Citation
Agarwal, Anish et al. "A Marketplace for Data: An Algorithmic Solution." 2019 ACM Conference on Economics and Computation, June 2019, Phoenix, Arizona, Association for Computing Machinery, June 2019. © 2019 Association for Computing Machinery.
Version
Author's final manuscript
Abstract
In this work, we aim to design a data marketplace; a robust real-time matching mechanism to efficiently buy and sell training data for Machine Learning tasks. While the monetization of data and pre-trained models is an essential focus of industry today, there does not exist a market mechanism to price training data and match buyers to sellers while still addressing the associated (computational and other) complexity. The challenge in creating such a market stems from the very nature of data as an asset: (i) it is freely replicable; (ii) its value is inherently combinatorial due to correlation with signal in other data; (iii) prediction tasks and the value of accuracy vary widely; (iv) usefulness of training data is difficult to verify a priori without first applying it to a prediction task. As our main contributions we: (i) propose a mathematical model for a two-sided data market and formally define the key associated challenges; (ii) construct algorithms for such a market to function and analyze how they meet the challenges defined. We highlight two technical contributions: (i) a new notion of "fairness" required for cooperative games with freely replicable goods; (ii) a truthful, zero regret mechanism to auction a class of combinatorial goods based on utilizing Myerson's payment function and the Multiplicative Weights algorithm. These might be of independent interest.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3328526.3329589
