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Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage voluntary participation in bearing blockchain costs. Moreover, the absence of framework simulations raises questions about the efficacy of these existing models. To solve above issues, this article introduces a fully third-party independent blockchain-driven contact tracing (BDCT) framework, detailed in its design. The BDCT framework features an RivestShamir-Adleman (RSA) encryption-based transaction verification method (RSA-TVM), achieving over 96% accuracy in contact case recording, even with a 60% probability of individuals failing to verify contact information. Furthermore, we propose a lightweight reputation corrected delegated proof of stake (RCDPoS) consensus mechanism, coupled with an incentive model, to ensure timely reporting of contact cases while maintaining blockchain decentralization. Additionally, a novel simulation environment for contact tracing is developed, accounting for three distinct contact scenarios with varied population density. Our results and discussions validate the effectiveness, robustness of the RSA-TVM and RC-DPoS, and the low storage demand of the BDCT framework. 

In monitoring station observation, for the best accuracy of rumor source detection, it is important to deploy monitors appropriately into the network. There are, however, a very limited number of studies on the monitoring station selection. This article will study the problem of detecting a single rumormonger based on an observation of selected infection monitoring stations in a complete snapshot taken at some time in an online social network (OSN) following the independent cascade (IC) model. To deploy monitoring stations into the observed network, we propose an influence-distancebased k-station selection method where the influence distance is a conceptual measurement that estimates the probability that a rumor-infected node can influence its uninfected neighbors. Accordingly, a greedy algorithm is developed to find the best k monitoring stations among all rumor-infected nodes with a 2-approximation. Based on the infection path, which is most likely toward the k infection monitoring stations, we derive that an estimator for the “most like” rumor source under the IC model is the Jordan infection center in a graph. Our theoretical analysis is presented in the article. The effectiveness of our method is verified through experiments over both synthetic and real-world datasets. As shown in the results, our k-station selection method outperforms off-the-shelf methods in most cases in the network under the IC model.