IEEE Communications Magazine - June 2017 - page 134

IEEE Communications Magazine • June 2017
132
0163-6804/17/$25.00 © 2017 IEEE
A
bstract
Mobile crowdsensing has emerged as an effi-
cient sensing paradigm that combines the crowd
intelligence and the sensing power of mobile
devices, such as mobile phones and Internet of
Things gadgets. This article addresses the con-
tradicting incentives of privacy preservation by
crowdsensing users, and accuracy maximization
and collection of true data by service providers.
We first define the individual contributions of
crowdsensing users based on the accuracy in
data analytics achieved by the service provid-
er from buying their data. We then propose a
truthful mechanism for achieving high service
accuracy while protecting privacy based on
user preferences. The users are incentivized to
provide true data by being paid based on their
individual contribution to the overall service
accuracy. Moreover, we propose a coalition
strategy that allows users to cooperate in pro-
viding their data under one identity, increasing
their anonymity privacy protection, and sharing
the resulting payoff. Finally, we outline import-
ant open research directions in mobile and peo-
ple-centric crowdsensing.
I
ntroduction
The proliferation of mobile devices with built-
in sensors has made mobile crowdsensing an
efficient sensing paradigm, especially in peo-
ple-centric and Internet of Things (IoT) services.
Crowdsensing users collect sensing data using
their personal mobile devices (e.g., mobile
phones and IoT gadgets). However, the devel-
opment of crowdsensing services is impeded by
many challenges, especially criticism of the pri-
vacy protection of crowdsensing users. Service
providers require true data, which is a key fac-
tor in optimizing data originated services [1]. This
introduces contradicting incentives of maximizing
the privacy protection of users and the prediction
accuracy of service providers. Most of the existing
incentive models in the literature are monetary
motivated with sole profit maximization objec-
tive (e.g., [2–4]), while the privacy incentive of
users is neglected. Therefore, conventional mon-
etary-based incentive models are inapplicable in
privacy preserving crowdsensing systems, and
new privacy-aware incentive models are required.
Several major questions related to developing pri-
vacy-aware incentive models in mobile crowd-
sensing arise. First, how does the crowdsensing
service define the contributions and payoff alloca-
tions of users with varying privacy levels? Second,
do crowdsensing coalitions change the attained
privacy of the cooperative users? Third, how
do cooperative users divide the coalition payoff
among themselves?
This article provides answers to the aforemen-
tioned questions by presenting a novel incentive
framework for privacy preservation and accuracy
maximization in mobile crowdsensing. Sensing
users select their preferred data anonymization
levels without knowing the privacy preferenc-
es of other users. The data anonymization is
inversely proportional to the accuracy of data
analytics of the service provider. Accordingly,
users are paid based on their marginal contri-
butions to service accuracy. Users can be also
penalized with a negative payoff if they cause
marginal harm to the service accuracy (e.g., an
outlier providing misleading data). Moreover, a
set of
k
cooperative users can jointly work by
forming a crowdsensing coalition, increasing the
anonymity privacy protection measured by the
k
-anonymity metric. The total coalition payoff
is then divided among the cooperative users
based on their marginal contributions to the
coalition’s data quality. Our experiments on a
real-world dataset of a crowdsensing activity rec-
ognition system show that the payoff allocation
of a particular user does not directly depend on
the contributed data size but on the data qual-
ity. Likewise, the payoff allocation is found to
decrease as the privacy level increases.
The rest of this article is organized as follows.
We first present an overview of mobile crowd-
sensing in people-centric and IoT services, and
review some related incentive mechanisms. Next,
we discuss the privacy preservation in mobile
crowdsensing. Then we propose an incentive
framework for privacy preservation and accura-
cy maximization in crowdsensing services. After
that, we present numerical experiments based
on a real-world crowdsensing dataset. Finally, we
outline some interesting research directions and
conclude the article.
M
obile
C
rowdsensing
and
I
o
T
This section first gives an overview of mobile
crowdsensing in IoT and then reviews some
monetary incentive mechanisms in mobile
crowdsensing.
The Accuracy-Privacy Trade-off of
Mobile Crowdsensing
Mohammad Abu Alsheikh, Yutao Jiao, Dusit Niyato, Ping Wang, Derek Leong, and Zhu Han
S
ustainable
I
ncentive
M
echanisms
for
M
obile
C
rowdsensing
The authors address the
contradicting incentives
of privacy preservation by
crowdsensing users and
accuracy maximization
and collection of true data
by service providers. They
define the individual con-
tributions of crowdsens-
ing users based on the
accuracy in data analytics
achieved by the service
provider from buying
their data. They propose
a truthful mechanism for
achieving high service
accuracy while protecting
privacy based on user
preferences.
Mohammad Abu Alsheikh, Yutao Jiao, Dusit Niyato, and Ping Wang are with Nanyang Technological University;
Derek Leong is with the Institute for Infocomm Research; Zhu Han is with the University of Houston.
Digital Object Identifier:
10.1109/MCOM.2017.1600737
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