TY - GEN
T1 - Aggregating crowd wisdom via blockchain: A private, correct, and robust realization
AU - Duan, Huayi
AU - Zheng, Yifeng
AU - Du, Yuefeng
AU - Zhou, Anxin
AU - Wang, Cong
AU - Au, Man Ho
N1 - Funding Information:
We thank our shepherd, Arno Wacker, and the anonymous reviewers for their helpful comments. This work was supported in part by the Research Grants Council of Hong Kong under Grant CityU 11276816, Grant CityU 11212717, and Grant CityU C1008-16G, by the Innovation and Technology Commission of Hong Kong under ITF Project ITS/168/17, and by the National Natural Science Foundation of China under Grant 61572412 and Grant 61602396.
Funding Information:
That said, ZebraLancer and our design are highly complementary to each other. A combination of them will be an interesting research direction that will yield better security for blockchain-based crowdsourcing and crowdsensing. IX. CONCLUSION We present a new blockchain-powered crowdsensing system in this paper. By a careful consolidation of techniques and customized designs, our framework achieves strong security guarantee with data confidentiality, differential privacy, service correctness, as well as robustness, in the open and distributed setting. The efficiency and practicality of our designs are also demonstrated by extensive experiments. We hope the proposed system can spur the otherwise wary users and the wide adoption of crowdsensing paradigm. ACKNOWLEDGMENT We thank our shepherd, Arno Wacker, and the anonymous reviewers for their helpful comments. This work was supported in part by the Research Grants Council of Hong Kong under Grant CityU 11276816, Grant CityU 11212717, and Grant CityU C1008-16G, by the Innovation and Technology Commission of Hong Kong under ITF Project ITS/168/17, and by the National Natural Science Foundation of China under Grant 61572412 and Grant 61602396.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.
AB - Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.
KW - Blockchain
KW - Crowdsensing
KW - Data Confidentiality
KW - Differential Privacy
KW - Smart Contract
KW - Trusted Hardware
KW - Zero-Knowledge Proof
UR - https://www.scopus.com/pages/publications/85070209410
U2 - 10.1109/PERCOM.2019.8767412
DO - 10.1109/PERCOM.2019.8767412
M3 - Conference article published in proceeding or book
AN - SCOPUS:85070209410
T3 - 2019 IEEE International Conference on Pervasive Computing and Communications, PerCom 2019
BT - 2019 IEEE International Conference on Pervasive Computing and Communications, PerCom 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Pervasive Computing and Communications, PerCom 2019
Y2 - 12 March 2019 through 14 March 2019
ER -