2017/02/20 by Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong +2 · 37 citations
Computer Science · Social Sciences · #Analytics #Bundle #Dynamic pricing #Information privacy #Mobile Crowdsensing and Crowdsourcing #Outsourcing #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Profit (economics) #Profit maximization #Service provider #Shapley value #cs.GT
paper · pdf · doi:10.1109/jsac.2017.2680845
published in IEEE Journal on Selected Areas in Communications 35(4), 906-920 (Institute of Electrical and Electronics Engineers) · 16 pages
arxiv created 2017/02/20 · openalex created_date 2017/03/16 · openalex publication_date 2017/03/16 · arxiv updated 2017/03/21 · openalex updated_date 2026/08/06
With the emerging sensing technologies, such as mobile crowdsensing and Internet of Things, people-centric data can be efficiently collected and used for analytics and optimization purposes. These data are typically required to develop and render people-centric services. In this paper, we address the privacy implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared with the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.