Privacy Management and Optimal Pricing in People-Centric Sensing

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Privacy Management and Optimal Pricing in People-Centric Sensing
Title:
Privacy Management and Optimal Pricing in People-Centric Sensing
Other Titles:
IEEE Journal on Selected Areas in Communications
Keywords:
Publication Date:
01 April 2017
Citation:
M. Abu Alsheikh, D. Niyato, D. Leong, P. Wang and Z. Han, "Privacy Management and Optimal Pricing in People-Centric Sensing," in IEEE Journal on Selected Areas in Communications, vol. 35, no. 4, pp. 906-920, April 2017. doi: 10.1109/JSAC.2017.2680845
Abstract:
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.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
0733-8716
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