TY - GEN

T1 - Reliable and Streaming Truth Discovery in Blockchain-based Crowdsourcing

AU - Mukkamala, Prasanna Siddharth

AU - Wu, Haiqin

AU - Dudder, Boris

N1 - Publisher Copyright: © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - Truth discovery is an effective and compelling approach to addressing data conflicts among different workers and offers more trustworthy truths to task requesters in crowdsourcing. Prior research either focused on studying more accurate truth discovery algorithms or aimed to protect data privacy from the centralized and honest-but-curious crowdsourcing platforms. They all overlooked the stronger threats from the malicious crowdsourcing platform (e.g., may return incorrectly estimated truths) and many critical issues inherited from centralization. This paper proposes a blockchain-based decentralized truth discovery scheme for crowdsourcing, with computation integrity guarantees against malicious participants and support for efficient processing of generic streaming data. We adopt the idea of hybrid storage and computations to ease the expensive on-chain cost. Workers are grouped for off-chain partial truth estimation and smart contracts are leveraged for on-chain final truth aggregation. To prevent any improper computations from malicious entities, we record the hashes of data and worker weights on-chain occasionally. Through theoretical analysis and extensive experiments over real-world and synthetic datasets implemented in Ethereum, we demonstrate that our scheme 1) achieves our reliability goals with certain privacy assurance; 2) exhibits a higher truth estimation accuracy than existing approaches and a lower gas consumption than the baseline.

AB - Truth discovery is an effective and compelling approach to addressing data conflicts among different workers and offers more trustworthy truths to task requesters in crowdsourcing. Prior research either focused on studying more accurate truth discovery algorithms or aimed to protect data privacy from the centralized and honest-but-curious crowdsourcing platforms. They all overlooked the stronger threats from the malicious crowdsourcing platform (e.g., may return incorrectly estimated truths) and many critical issues inherited from centralization. This paper proposes a blockchain-based decentralized truth discovery scheme for crowdsourcing, with computation integrity guarantees against malicious participants and support for efficient processing of generic streaming data. We adopt the idea of hybrid storage and computations to ease the expensive on-chain cost. Workers are grouped for off-chain partial truth estimation and smart contracts are leveraged for on-chain final truth aggregation. To prevent any improper computations from malicious entities, we record the hashes of data and worker weights on-chain occasionally. Through theoretical analysis and extensive experiments over real-world and synthetic datasets implemented in Ethereum, we demonstrate that our scheme 1) achieves our reliability goals with certain privacy assurance; 2) exhibits a higher truth estimation accuracy than existing approaches and a lower gas consumption than the baseline.

KW - blockchain

KW - crowdsourcing

KW - data stream

KW - reliability

KW - Truth discovery

UR - http://www.scopus.com/inward/record.url?scp=85177454688&partnerID=8YFLogxK

U2 - 10.1109/SECON58729.2023.10287465

DO - 10.1109/SECON58729.2023.10287465

M3 - Article in proceedings

AN - SCOPUS:85177454688

SP - 492

EP - 500

BT - 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023

PB - IEEE Computer Society Press

T2 - 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023

Y2 - 11 September 2023 through 14 September 2023

ER -