Collaborative Streaming: Trust Requirements for Price Sharing

Grubenmann, Tobias; Dell'Aglio, Daniele; Bernstein, Abraham

Abstract

Stream Processing (SP) is an important Big Data technology enabling continuous querying of data streams. The stream setting offers the opportunity to exploit synergies and, theoretically, share the access and processing costs between multiple different collaborators. But what should be the monetary contribution of each consumer when they do not trust each other and have varying valuations of the differing outcomes? In this article, we present Collaborative Stream Processing (CSP), a model where the costs, which are set exogenously by providers, are shared between multiple consumers, the collaborators. For this, we identify three important requirements for CSP to establish trust between the collaborators and propose a CSP algorithm, ENCSPA, adhering to these requirements. Based on the collaborators' outcome valuations and the costs of the raw data streams, ENCSPA computes the payment for each collaborator. At the same time, ENCSPA ensures that no collaborator has an incentive to manipulate the system by providing misinformation about her/his value, budget, or time limit. We show that ENCSPA can calculate payments in a reasonable amount of time for up to one thousand collaborators.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2019 IEEE International Conference on Big Data (Big Data)
• Start Date: Dec 9, 2019
• End Date: Dec 12, 2019
• Online Publication Date: Feb 24, 2020
• Publication Date: 2019-12
• Deposit Date: Jun 3, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2019 IEEE International Conference on Big Data (Big Data)
• DOI: https://doi.org/10.1109/bigdata47090.2019.9005470
• Keywords: Trust, Big Data, Stream Processing, Cost Sharing