zkCrowd: a hybrid blockchain-based crowdsourcing platform
Abstract
Blockchain, a promising decentralized para-digm, can be exploited not only to overcome the shortcomings of the traditional crowdsourcing systems, but also to bring technical innovations, such as decentralization and accountability. Nevertheless, some critical inherent limitations of blockchain have been rarely addressed in the literature when it is incorporated into crowdsourcing, which may yield the performance bottleneck in the crowdsourcing systems. To further leverage the superiority of combining blockchain and crowdsourcing, in this article, we propose an innovative hybrid blockchain crowdsourcing platform, named zkCrowd. Our zkCrowd integrates with a hybrid blockchain structure, smart contract, dual ledgers, and dual consensus protocols to secure communications, verify transactions, and preserve privacy. Both the theoretical analysis and experiments are performed to evaluate the advantages of zkCrowd over the state of the art.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Crowdsourcing on the traditional platform is exposed to several shortcomings such as a single point of failure, controller's silent misbehaviour, a conflict of opinions between the task requesters and the workers.
“Crowdsourcing on the traditional platform is exposed to several shortcomings such as a single point of failure, controller's silent misbehaviour, a conflict of opinions between the task requesters and the workers [193].”
From A survey on the adoption of blockchain in IoT: challenges and solutions (2021) · cites this paper as [193]
Blockchain, a revolutionary decentralized model, can be adapted not only to remove the limitations of the conventional crowdsourcing schemes but also to usher technological advancements including decentralization and transparency.
“Blockchain, a revolutionary decentralized model, can be adapted not only to remove the limitations of the conventional crowdsourcing schemes but also to usher technological advancements including decentralization and transparency [193,195].”
From A survey on the adoption of blockchain in IoT: challenges and solutions (2021) · cites this paper as [193]
Research has investigated blockchain as a promising solution to the service outsourcing challenges such as security, privacy, payment and contract.
“Research [93,94,193] has investigated blockchain as a promising solution to the service outsourcing challenges such as security, privacy, payment and contract.”
From A survey on the adoption of blockchain in IoT: challenges and solutions (2021) · cites this paper as [193]
Cites
Practical Byzantine fault tolerance
Castro, M., & Liskov, B. H. (1999) · Operating Systems Design and Implementation · doi:10.5555/296806.296824 · Cited by 4,026 · see citing works
Decentralizing Privacy: Using Blockchain to Protect Personal Data
Zyskind, G., Nathan, O., & Pentland, A. '. (2015) · 2015 IEEE Security and Privacy Workshops · doi:10.1109/spw.2015.27 · Cited by 2,617 · see citing works
Hawk: The Blockchain Model of Cryptography and Privacy-Preserving Smart Contracts
Kosba, A., Miller, A., Shi, E., Wen, Z., & Papamanthou, C. (2016) · 2016 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2016.55 · Cited by 2,227 · see citing works
SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies
Bonneau, J., Miller, A. E., Clark, J., Narayanan, A., Kroll, J. A., & Felten, E. W. (2015) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2015.14 · Cited by 1,262 · see citing works
On Scaling Decentralized Blockchains
Croman, K., Decker, C., Eyal, I., Gencer, A. E., Juels, A., Kosba, A. E., Miller, A. E., Saxena, P., Shi, E., Sirer, E. G., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-53357-4_8 · Cited by 1,199 · see citing works
Casper the Friendly Finality GadgetPreprint
Buterin, V., & Griffith, V. (2017) · arXiv (Cornell University) · doi:10.4230/lipics.aft.2023.30 · Cited by 601 · see citing works
SNARKs for C: Verifying Program Executions Succinctly and in Zero Knowledge
Ben-Sasson, E., Chiesa, A., Genkin, D., Tromer, E., & Virza, M. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40084-1_6 · Cited by 598 · see citing works
Crowdbc: a blockchain-based decentralized framework for crowdsourcing
Li, M., Weng, J., Yang, A., Lu, W., Zhang, Y., Hou, L., Liu, J.-N., Xiang, Y., & Deng, R. H. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2881735 · Cited by 522 · see citing works
Sukhwani, H., Martínez, J. M., Chang, X., Trivedi, K. S., & Rindos, A. (2017) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds.2017.36 · Cited by 411 · see citing works
Practical Byzantine fault tolerance
de Castro, O. T., & Miguel, M. D. (2000) · Cited by 367 · see citing works
Differentially Private Data Publishing and Analysis: A Survey
Zhu, T., Li, G., Zhou, W., & Yu, P. S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2697856 · Cited by 330 · see citing works
Reputation-based incentive protocols in crowdsourcing applications
Zhang, Y., & van der Schaar, M. (2012) · 2012 Proceedings IEEE INFOCOM · doi:10.1109/infcom.2012.6195597 · Cited by 315 · see citing works
Salehi, N., Irani, L. C., Bernstein, M. S., Alkhatib, A., Ogbe, E., Milland, K., & Clickhappier (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702508 · Cited by 274 · see citing works
A Proof-of-Trust Consensus Protocol for Enhancing Accountability in Crowdsourcing Services
Zou, J., Ye, B., Qu, L., Wang, Y., Orgun, M. A., & Li, L. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2018.2823705 · Cited by 226 · see citing works
ZebraLancer: Private and Anonymous Crowdsourcing System atop Open Blockchain
Lu, Y., Tang, Q., & Wang, G. (2018) · 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00087 · Cited by 224 · see citing works
Parno (2016) · doi:10.1145/2856449 · Cited by 212 · see citing works
IKP: Turning a PKI Around with Decentralized Automated Incentives
Matsumoto, S., & Reischuk, R. M. (2017) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2017.57 · Cited by 174 · see citing works
A Blockchain-Based Location Privacy Protection Incentive Mechanism in Crowd Sensing Networks
Jia, B., Zhou, T., Li, W., Liu, Z., & Zhang, J. (2018) · Sensors · doi:10.3390/s18113894 · Cited by 126 · see citing works
Self-citationNonoutsourceable Scratch-Off Puzzles to Discourage Bitcoin Mining Coalitions
Miller, A. E., Kosba, A. E., Katz, J. I., & Shi, E. (2015) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2810103.2813621 · Cited by 114 · see citing works
Keep Your Promise: Mechanism Design Against Free-Riding and False-Reporting in Crowdsourcing
Zhang, X., Xue, G., Yu, R., Yang, D., & Tang, J. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2441031 · Cited by 88 · see citing works
Compounding of Wealth in Proof-of-Stake Cryptocurrencies
Fanti, G. C., Kogan, L., Oh, S., Ruan, K., Viswanath, P., & Wang, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32101-7_3 · Cited by 79 · see citing works
Privacy Preserving and Cost Optimal Mobile Crowdsensing Using Smart Contracts on Blockchain
Chatzopoulos, D., Gujar, S., Faltings, B. V., et al. (2018) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass.2018.00068 · Cited by 69 · see citing works
The Novel Location Privacy-Preserving CKD for Mobile Crowdsourcing Systems
Chi, Z., Wang, Y., Huang, Y., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2783322 · Cited by 43 · see citing works
An Integrated Reward and Reputation Mechanism for MCS Preserving Users’ Privacy
Tanas, C., Delgado-Segura, S., & Herrera-Joancomartí, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29883-2_6 · Cited by 37 · see citing works
A differentially k-anonymity-based location privacy-preserving for mobile crowdsourcing systems
Wang, Y., Cai, Z., Chi, Z., et al. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.03.040 · Cited by 25 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A survey on the adoption of blockchain in IoT: challenges and solutions
Uddin, M. A., Stranieri, A., Gondal, I., & Balasubramanian, V. (2021) · Blockchain: Research and Applications · doi:10.1016/j.bcra.2021.100006 · Cited by 365 · see citing works
- Authors
- Zhu, S., Cai, Z., Hu, H., Li, Y., & Li, W.
- Affiliations
- Georgia State University (US)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-06
- Volume
- 16
- Issue
- 6
- Pages
- 4196-4205
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2973889821
- Cited by
- 206
- References
- 25
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 27 · 2023: 38 · 2022: 38 · 2021: 55 · 2020: 28 · 2019: 3
- FWCI
- 38.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Science Foundation (1829674, 1741277, 1912753, 1704287)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- blockchain, crowdsourcing, hybrid blockchain, smart contracts, privacy preservation, transaction verification, decentralized systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
