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Privacy-preserving task assignment is vital to assign a task to appropriate workers and protect workers' privacy or task privacy for spatial crowdsourcing (SC). Existing solutions usually require each worker to travel to the task location on purpose to perform this task, which fails to consider that workers have specific trajectories and carry out the task on their way in a hitchhiking manner. To this end, this paper proposes a privacy-preserving hitchhiking task assignment scheme for SC, named PKGS. Specifically, we formulate the privacy-preserving hitchhiking task assignment as a decision problem of the relationship between dot and line under privacy protection. In particular, we present a privacy-preserving travel distance calculation protocol and a privacy-preserving comparison protocol through the Paillier cryptosystem and the SC framework. Results of theoretical analysis and experimental evaluation show that PKGS can not only protect the location privacy of both each worker and the task simultaneously but also assign the task to the worker holding a minimum travel distance. In contrast to prior solutions, PKGS outperforms in the computation of travel distance and task assignment. [ABSTRACT FROM AUTHOR]

Vehicle ad-hoc network (VANET) is interconnected through message forwarding and exchanging among vehicle nodes. Due to its highly dynamic topology and its wireless and heterogeneous communication mode, VANET is more vulnerable to security threats from multiple parties. Compared to entity-based security authentication, it is essential to consider how to protect the security of the data itself. Existing studies have evaluated the reliability of interactive data through reputation quantification, but there are still some issues in the design of secure reputation management schemes, such as its low efficiency, poor security, and unreliable management. Aiming at the above-mentioned issues, in this paper we propose an effective VANET model with a secure reputation based on a blockchain, and it is called the double-layer blockchain-based reputation evaluation & management model (DBREMM). In the DBREMM, we design a reputation management model based on two parallel blockchains that work collaboratively, and these are called the event chain and reputation chain. A complete set of reputation evaluation schemes is presented. Our schemes can reduce observation errors and improve evaluation reliability during trust computation by using direct trust calculation based on the multi-factor Bayesian inference. Additionally, we propose an indirect trust calculation based on the historical accumulated reputation value with an attenuation factor, and a secure a reputation fusion scheme based on the number threshold with the fluctuation factor, which can reduce the possibility of attacks, such as collusive attacks and false information injection. Theoretical analysis and extensive simulation experiments reflect the DBREMM's security algorithm effectiveness, accuracy, and ability to resist several attacks. [ABSTRACT FROM AUTHOR]