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The public blockchain was originally conceived to process monetary transactions in a peer-to-peer network while preventing double-spending. It has since been extended to numerous other applications including execution of programs that exist on the blockchain called smart contracts. Smart contracts have a major limitation, namely they only operate on data that is on the blockchain. Trusted entities called oracles attest to external data in order to bring it onto the blockchain but they do so without the robust security guarantees that blockchains generally provide. This has the potential to turn oracles into centralized points-of-failure. To address this concern, this paper introduces Astraea, a decentralized oracle based on a voting game that decides the truth or falsity of propositions. Players fall into two roles: voters and certifiers. Voters play a low-risk/low-reward role that is resistant to adversarial manipulation while certifiers play a high-risk/high-reward role so they are required to play with a high degree of accuracy. This paper also presents a formal analysis of the parameters behind the system to measure the probability of an adversary with bounded funds being able to successfully manipulate the oracles decision, that shows that the same parameters can be set to make manipulation arbitrarily difficult---a desirable feature for the system. Further, this analysis demonstrates that under those conditions a Nash equilibrium exists where all rational players are forced to behave honestly.
Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keep
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The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves data island problems caused by data privacy concerns. However, large-scale FEL still faces