TY - JOUR
T1 - Privacy Management and Optimal Pricing in People-Centric Sensing
AU - Abu Alsheikh, Mohammad
AU - Niyato, Dusit
AU - Leong, Derek
AU - Wang, Ping
AU - Han, Zhu
PY - 2017/4/1
Y1 - 2017/4/1
N2 - 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.
AB - 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.
KW - Data privacy
KW - mobile crowdsensing
KW - participatory sensing
KW - people-centric sensing
KW - service pricing
UR - http://www.scopus.com/inward/record.url?scp=85019769162&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/privacy-management-optimal-pricing-peoplecentric-sensing
U2 - 10.1109/JSAC.2017.2680845
DO - 10.1109/JSAC.2017.2680845
M3 - Article
AN - SCOPUS:85019769162
SN - 0733-8716
VL - 35
SP - 906
EP - 920
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 4
M1 - 7879223
ER -