Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight.

Public goods projects, including open source technology, client development, and blockchain knowledge education, play an important role in the flourishing blockchain ecosystem. Accordingly, decision making for public goods funding is a key issue in the studies of the blockchain ecosystem. This work develops a human oracle protocol approach, involved with public goods projects, experts, and funders, as a solution to the public goods investment problem on blockchain. In our human oracle, funders contribute their investments, which are stored in a funding pool. Experts provide investment advice on public goods projects based on their experience. Decisions made by the human oracle on the amount of support from the funding pool are based on experts’ reputation. The reputation of each expert is updated by the performance of the project’s implementation in comparison to her advice. That is, better investment performance brings a higher reputation. Besides being applied to static model, our human oracle can also be extended to accommodate dynamic settings, in which the experts might leave or join the decision-making process. We introduce a regret bound to measure the effectiveness of our human oracle. Theoretically, we prove an upper regret bound for both static and dynamic models, and prove its tightness with an asymptotically equal lower bound. Empirically, we show that our oracle’s investment decision is close to the optimal investment in hindsight.

Blockchain protocols require nodes to verify all received transactions before forwarding them. However, massive spam transactions cause the participants in blockchain systems to consume many resources in verifying and propagating transactions. This paper proposes a reputation-based mechanism to increase the efficiency of processing transactions by considering the reputations of the sending nodes. Reputations are in turn adjusted based on the quality of transaction processing. Our proposed reputation-based mechanism offers three main contributions. First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified. Second, we optimize the transaction forwarding protocol to reduce propagation delay by prioritizing forwarding transactions to reputable receivers. Third, we design a data request protocol that provides alternative data exchange methods for nodes with different reputations. A series of simulations demonstrate the performance of our reputation-based mechanism.

For a blockchain system, the network layer is of great importance for scalability and security. The critical task of blockchain networks is to provide a fast delivery of data. A rapid spread accelerates the transactions to be included into blocks and then confirmed. Existing blockchain systems, especially the cryptocurrencies like Bitcoin, take a simple strategy that requires relay nodes to verify all received transactions and then forward valid ones to all outbound neighbors. Unfortunately, this design is inefficient and slows down the transmission of transactions. In this paper, we introduce the concept of reputation and propose a novel relay protocol, RepuLay, to accelerate the transmission of transactions across the network. First of all, we design a reputation mechanism to help each node identify the unreliable and inactive neighbors. In this mechanism, two values are used to define one’s reputation. Each node keeps a local list of reputations of all its neighbors. Based on the reputation mechanism, RepuLay adopts probabilistic strategies to process transactions. More specifically, after receiving a transaction, the relay node verifies it with a certain probability, which is deduced from the first value of sender’s reputation. Next, the valid and unverified transactions are forwarded to some neighbors. Each neighbor has some probability to be chosen as a receiver and the probability is determined by its second value of reputation. Theoretically, we prove that our design can guarantee the quality of relayed transactions. Further simulation results confirm that RepuLay effectively accelerates the spread of transactions and optimize the usage of nodes’ bandwidths.

As one of the fastest-growing applications in the Peer-to-Peer (P2P) network, the development of blockchain technology is accompanied by different attacks. Those include whitewashing, free-riding, and distributed denial of service (DDoS) attacks, particularly because of features such as anonymity, distributed, permissionless in the blockchain network. One popular of them is spam transactions. Although the blockchain protocol requires each node to verify all received transactions, many nodes choose to forward transactions without verification to conserve their computational power, as there is no punishment for such a shirking. And it makes the blockchain vulnerable to the spreading of spam transactions over the network and creates extra burdens for all nodes in the network. We propose a reputation mechanism for the blockchain system to tackle this problem: Each node will locally compute reputations of its neighbors, and decide the probability to verify a received transaction based on the reputation value of the transaction sender. In turn, its neighbors will have an incentive to conduct verification to keep its reputation high. Subsequently, spam transactions can be blocked before reaching the miners. We have conducted a series of simulations, which clearly demonstrate the advantage of our reputation mechanism.

Summary As the value of data has received considerable attention, data trading shows broad market prospects. The existing data trading methods, including private trades and centralized trades, have high risks regarding transaction security and data protection. To solve this problem, we propose a decentralized trading solution for open fair data trading by deploying the smart contract on the blockchain network. The data for sale are encrypted and stored on the distributed storage platform but not directly on the blockchain network. Because the trading content is the decryption key of the data, the proposed new method can alleviate the storage pressure of the blockchain by reducing the transaction cost. We conduct a security analysis which shows that our scheme achieves secure, practical, open, and fair trading. We implement our trading contract with solidity and test it on the Ethereum's test network, and extensive experiments demonstrate desirable feasibility of our proposal.