TY - JOUR
T1 - LPTD
T2 - Achieving lightweight and privacy-preserving truth discovery in CIoT
AU - Zhang, Chuan
AU - Zhu, Liehuang
AU - Xu, Chang
AU - Sharif, Kashif
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/1
Y1 - 2019/1
N2 - In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices. In CIoT, truth discovery plays an important role in identifying truthful values from large scale data to help CIoT provide deeper insights and value from collected information. However, the privacy concerns of IoT devices pose a major challenge in designing truth discovery approaches. Although existing schemes of truth discovery can be executed with strong privacy guarantees, they are not efficient or cannot be applied in real-life CIoT applications. This article proposes a novel framework for lightweight and privacy-preserving truth discovery called LPTD-I, which is implemented by incorporating fog and cloud platforms, and adopting the homomorphic Paillier encryption and one-way hash chain techniques. This scheme not only protects devices’ privacy, but also achieves high efficiency. Moreover, we introduce a fault tolerant (LPTD-II) framework which can effectively overcome malfunctioning CIoT devices. Detailed security analysis indicates the proposed schemes are secure under a comprehensively designed threat model. Experimental simulations are also carried out to demonstrate the efficiency of the proposed schemes.
AB - In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices. In CIoT, truth discovery plays an important role in identifying truthful values from large scale data to help CIoT provide deeper insights and value from collected information. However, the privacy concerns of IoT devices pose a major challenge in designing truth discovery approaches. Although existing schemes of truth discovery can be executed with strong privacy guarantees, they are not efficient or cannot be applied in real-life CIoT applications. This article proposes a novel framework for lightweight and privacy-preserving truth discovery called LPTD-I, which is implemented by incorporating fog and cloud platforms, and adopting the homomorphic Paillier encryption and one-way hash chain techniques. This scheme not only protects devices’ privacy, but also achieves high efficiency. Moreover, we introduce a fault tolerant (LPTD-II) framework which can effectively overcome malfunctioning CIoT devices. Detailed security analysis indicates the proposed schemes are secure under a comprehensively designed threat model. Experimental simulations are also carried out to demonstrate the efficiency of the proposed schemes.
KW - CIoT
KW - Lightweight
KW - Privacy-preserving
KW - Truth discovery
UR - http://www.scopus.com/inward/record.url?scp=85051024853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051024853&partnerID=8YFLogxK
U2 - 10.1016/j.future.2018.07.064
DO - 10.1016/j.future.2018.07.064
M3 - Article
AN - SCOPUS:85051024853
SN - 0167-739X
VL - 90
SP - 175
EP - 184
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -