research-article

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

Author:

Raul Castro FernandezAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 172, Pages 1 - 25

https://doi.org/10.1145/3589317

Published: 20 June 2023 Publication History

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.

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Index Terms

Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Data management systems

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

Editor:
Divyakant Agrawal
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Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

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Fan LZhang SKong YYi XWang YXu XYu CLi HShi Y(2024)Evaluating the Privacy Valuation of Personal Data on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785098:3(1-33)Online publication date: 9-Sep-2024
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