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In recent years, cognitive Internet of Things (CIoT) has receivedconsiderable attention because it can extract valuable information from variousInternet of Things (IoT) devices. In CIoT, truth discovery plays an importantrole in identifying truthful values from large scale data to help CIoT providedeeper insights and value from collected information. However, the privacyconcerns of IoT devices pose a major challenge in designing truth discoveryapproaches. Although existing schemes of truth discovery can be executed withstrong privacy guarantees, they are not efficient or cannot be applied inreal-life CIoT applications. This article proposes a novel framework forlightweight and privacy-preserving truth discovery called LPTD-I, which isimplemented by incorporating fog and cloud platforms, and adopting thehomomorphic Paillier encryption and one-way hash chain techniques. This schemenot only protects devices' privacy, but also achieves high efficiency.Moreover, we introduce a fault tolerant (LPTD-II) framework which caneffectively overcome malfunctioning CIoT devices. Detailed security analysisindicates the proposed schemes are secure under a comprehensively designedthreat model. Experimental simulations are also carried out to demonstrate theefficiency of the proposed schemes.

LPTD: Achieving Lightweight and Privacy-Preserving Truth Discovery in CIoT