Data sharing and exchanging with incentive and optimization: a survey

Author:

Liu Liyuan,Han Meng

Abstract

AbstractAs the landscape of big data evolves, the paradigm of data sharing and exchanging has gained paramount importance. Nonetheless, the transition to efficient data sharing and exchanging is laden with challenges. One of the principal challenges is incentivizing diverse users to partake in the data sharing and exchange process. Users, especially those in potential competitive positions, often exhibit reluctance towards sharing or exchanging their data, particularly if they perceive the rewards as inadequate. Given this context, it’s imperative to institute an incentive mechanism that’s not only computationally efficient and secure but also provides both monetary and trustworthy inducements. This study introduces a taxonomy of incentive-based data sharing and exchanging, structured around its lifecycle, and elucidates the challenges inherent in each phase. We classify incentive mechanisms into monetary and non-monetary categories, postulating that the concomitant use of both types of incentives is more effective for data sharing and exchanging applications. Subsequent sections provide an overview of extant literature pertinent to each phase of the data sharing and exchanging lifecycle. In conclusion, we underscore the prevailing challenges in this domain and advocate for intensified efforts to refine the design of incentive mechanisms in data sharing and exchanging.

Publisher

Springer Science and Business Media LLC

Reference187 articles.

1. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH. Big data: the next frontier for innovation, competition. Washington, DC: McKinsey Global Institute; 2011.

2. Wikipedia. Information exchange. Wikipedia, 2015.

3. Wiggers K. Amazon launches AWS Data Exchange for tracking and sharing data sets. VentureBeat, 2019.

4. Moreno-Guerrero A-J, Rodríguez-Jiménez C, Ramos-Navas-Parejo M, Soler-Costa R, López-Belmonte J. Whatsapp and google drive influence on pre-service students’ learning. In: Frontiers in education, vol. 5, p. 152. Frontiers Media SA; 2020.

5. Zhang X, Yang Z, Sun W, Liu Y, Tang S, Xing K, Mao X. Incentives for mobile crowd sensing: a survey. IEEE Commun Surv Tutor. 2015;18(1):54–67.

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