CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing

By Ming Li, Jian Weng, Anjia Yang, Wei Lu, Yue Zhang, Lin Hou, Jia-Nan Liu, Yang Xiang, Robert Huijie Deng. IEEE Transactions on Parallel and Distributed Systems, 2019

461 citations recorded by the source corpus.

Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized framework for crowdsourcing named CrowdBC, in which a requester's task can be solved by a crowd of workers without relying on any third trusted institution, users’ privacy can be guaranteed and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, based on which we give a concrete scheme. We further implement a software prototype on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability, and scalability of our proposed crowdsourcing system.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/ef822daf940422931d1e8acf819e1634c0c30901

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dusist Niyato
2. Zehui Xiong
3. Lingjuan Lyu
4. Jun Zhao
5. Ping Wang
6. Yonggang Wen
7. Rui Tan
8. Peizhao Hu

In Computer Science Applications

1. Jun Zhao
2. Dusist Niyato
3. Lingjuan Lyu

In Information Systems

1. Dusist Niyato
2. Zehui Xiong
3. Jun Zhao
4. Ping Wang
5. Yonggang Wen
6. Peizhao Hu
7. Wenbo Wang
8. Dinh Thai Hoang

Citations in the corpus

• Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2019, background): “Li et al. [25] designed a decentralized framework based on blockchain for crowdsourcing tasks, which enabled them to do crowdsourcing tasks without a centralized server.”
• Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System (2019, background): “[11] designed a blockchain-based decentralized framework for crowdsourcing, which enables crowdsourcing without the centralized server.”
• An Overview on Smart Contracts: Challenges, Advances and Platforms (2019, unknown): “In summary, blockchain technology has the key characteristics of decentralization, immutable, persistency and anonymity [26], [27], [28].”
• A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks (2018, methodology): “In [205], a purely decentralized crowdsourcing system for general purpose is proposed following the paradigm described by Figure 25.”