Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia

Author:

Castro Fernandez Raul1ORCID

Affiliation:

1. The University of Chicago, Chicago, IL, USA

Abstract

Organizations that would mutually benefit from pooling their data are otherwise wary of sharing. This is because sharing data is costly-in time and effort-and, at the same time, the benefits of sharing are not clear. Without a clear cost-benefit analysis, participants default in not sharing. As a consequence, many opportunities to create valuable data-sharing consortia never materialize, and the value of data remains locked. We introduce a new sharing model, market protocol, and algorithms to incentivize the creation of data-sharing markets. The combined contributions of this paper, which we call DSC, incentivize the creation of data-sharing markets that unleash the value of data for its participants. The sharing model introduces two incentives; one that guarantees that participating is better than not doing so and another that compensates participants according to how valuable their data is. Because operating the consortia is costly, we are also concerned with ensuring its operation is sustainable: we design a protocol that ensures that a valuable data-sharing consortium forms when it is sustainable. We introduce algorithms to elicit the value of data from the participants, which is used first to cover the costs of operating the consortia and second to compensate for data contributions. For the latter, we challenge using the Shapley value to allocate revenue. We offer analytical and empirical evidence for this and introduce an alternative method that compensates participants better and leads to the formation of data-sharing consortia.

Funder

NSF

Publisher

Association for Computing Machinery (ACM)

Reference73 articles.

1. Daniel Abadi , Owen Arden , Faisal Nawab , and Moshe Shadmon . 2020 . Anylog: a grand unification of the internet of things . In Conference on Innovative Data Systems Research (CIDR ?20) . Daniel Abadi, Owen Arden, Faisal Nawab, and Moshe Shadmon. 2020. Anylog: a grand unification of the internet of things. In Conference on Innovative Data Systems Research (CIDR ?20).

2. Jacob D Abernethy , Rachel Cummings , Bhuvesh Kumar , Sam Taggart , and Jamie H Morgenstern . 2019. Learning auctions with robust incentive guarantees. Advances in Neural Information Processing Systems 32 ( 2019 ). Jacob D Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, and Jamie H Morgenstern. 2019. Learning auctions with robust incentive guarantees. Advances in Neural Information Processing Systems 32 (2019).

3. A Marketplace for Data

4. Claudia Allen , Terrisca R Des Jardins , Arvela Heider, Kristin A Lyman, Lee McWilliams, Alison L Rein, Abigail A Schachter, Ranjit Singh, Barbara Sorondo, Joan Topper, et al . 2014 . Data governance and data sharing agreements for community-wide health information exchange: lessons from the beacon communities. EGEMS 2, 1 (2014). Claudia Allen, Terrisca R Des Jardins, Arvela Heider, Kristin A Lyman, Lee McWilliams, Alison L Rein, Abigail A Schachter, Ranjit Singh, Barbara Sorondo, Joan Topper, et al . 2014. Data governance and data sharing agreements for community-wide health information exchange: lessons from the beacon communities. EGEMS 2, 1 (2014).

5. Nuno Antonio , Ana de Almeida , and Luis Nunes . 2019. Hotel booking demand datasets. Data in brief 22 ( 2019 ), 41--49. Nuno Antonio, Ana de Almeida, and Luis Nunes. 2019. Hotel booking demand datasets. Data in brief 22 (2019), 41--49.

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