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ZebraLancer: Private and Anonymous Crowdsourcing System atop Open Blockchain
Abstract
We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer, and overcome two fundamental challenges of decentralizing crowdsourcing, i.e. data leakage and identity breach. First, our outsource-then-prove methodology resolves the tension between blockchain transparency and data confidentiality, which is critical in crowdsourcing use-case. ZebraLancer ensures: (i) a requester will not pay more than what data deserve, according to a policy announced when her task is published via the blockchain; (ii) each worker indeed gets a payment based on the policy, if he submits data to the blockchain; (iii) the above properties are realized not only without a central arbiter, but also without leaking the data to the open blockchain. Furthermore, the transparency of blockchain allows one to infer private information about workers and requesters through their participation history. On the other hand, allowing anonymity will enable a malicious worker to submit multiple times to reap rewards. ZebraLancer overcomes this problem by allowing anonymous requests/submissions without sacrificing the accountability. The idea behind is a subtle linkability: if a worker submits twice to a task, anyone can link the submissions, or else he stays anonymous and unlinkable across tasks. To realize this delicate linkability, we put forward a novel cryptographic concept, i.e. the common-prefix-linkable anonymous authentication. We remark the new anonymous authentication scheme might be of independent interest. Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum. The experiment results show the applicability of our protocol with the existing real-world blockchain.
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Metadata
- Authors
- Lu, Y., Tang, Q., & Wang, G.
- Affiliations
- New Jersey Institute of Technology (US)
- Venue
- 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 853-865
- OpenAlex
- W2790257806
- Cited by
- 224
- References
- 46
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 28 · 2023: 26 · 2022: 38 · 2021: 47 · 2020: 31 · 2019: 26 · 2018: 6
- FWCI
- 21.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Distributed Computing Systems (ICDCS) (publishedVersion)
- 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 › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- blockchain, data privacy, data leakage, linkability, unlinkability, anonymous authentication, Ethereum, image annotation
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.
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