TY - GEN
T1 - Leveraging crowdsensed data streams to discover and sell knowledge: A secure and efficient realization
AU - Cai, Chengjun
AU - Zheng, Yifeng
AU - Wang, Cong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/19
Y1 - 2018/7/19
N2 - Leveraging the wisdom of crowd for knowledge discovery and monetization is increasingly popular nowadays. Among others, one popular way of leveraging the crowd wisdom is crowdsensing with truth discovery, which is able to discover truthful knowledge from the unreliable sensory data harvested from mobile clients. In order to become truly successful, however, a number of challenges are yet to be addressed. First, safeguarding clients' sensory data is demanded for privacy protection. Second, in many real crowdsensing applications, data are usually collected in a streaming manner, so truth discovery is naturally required to be efficiently conducted in a streaming fashion. Thirdly, knowledge monetization should be made full-fledged, endowed with features of transparency and streamlined processing while fully addressing the practical needs of parties in the monetization ecosystem. In this paper, we present our initial effort on a crowdsensing framework that enables privacy-preserving knowledge discovery and full-fledged blockchain-based knowledge monetization. Our framework enables privacy-preserving and efficient truth discovery over encrypted crowdsensed data streams for truthful knowledge discovery. Meanwhile, with careful integration of the newly emerging blockchain-based smart contract technology, our framework allows full-fledged knowledge monetization. Tackling the challenges of monetization fairness and (on-chain) knowledge confidentiality, our customized knowledge monetization design well respects the interests of knowledge seller and requester, with full support of transparency, streamlined processing, and automatic quality-aware rewards for clients. Extensive experiments on Microsoft Azure cloud and Ethereum blockchain demonstrate the practically affordable performance of our design.
AB - Leveraging the wisdom of crowd for knowledge discovery and monetization is increasingly popular nowadays. Among others, one popular way of leveraging the crowd wisdom is crowdsensing with truth discovery, which is able to discover truthful knowledge from the unreliable sensory data harvested from mobile clients. In order to become truly successful, however, a number of challenges are yet to be addressed. First, safeguarding clients' sensory data is demanded for privacy protection. Second, in many real crowdsensing applications, data are usually collected in a streaming manner, so truth discovery is naturally required to be efficiently conducted in a streaming fashion. Thirdly, knowledge monetization should be made full-fledged, endowed with features of transparency and streamlined processing while fully addressing the practical needs of parties in the monetization ecosystem. In this paper, we present our initial effort on a crowdsensing framework that enables privacy-preserving knowledge discovery and full-fledged blockchain-based knowledge monetization. Our framework enables privacy-preserving and efficient truth discovery over encrypted crowdsensed data streams for truthful knowledge discovery. Meanwhile, with careful integration of the newly emerging blockchain-based smart contract technology, our framework allows full-fledged knowledge monetization. Tackling the challenges of monetization fairness and (on-chain) knowledge confidentiality, our customized knowledge monetization design well respects the interests of knowledge seller and requester, with full support of transparency, streamlined processing, and automatic quality-aware rewards for clients. Extensive experiments on Microsoft Azure cloud and Ethereum blockchain demonstrate the practically affordable performance of our design.
KW - Blockchain
KW - Crowdsensing
KW - Truth discovery
UR - http://www.scopus.com/inward/record.url?scp=85050958082&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2018.00064
DO - 10.1109/ICDCS.2018.00064
M3 - Conference article published in proceeding or book
AN - SCOPUS:85050958082
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 589
EP - 599
BT - Proceedings - 2018 IEEE 38th International Conference on Distributed Computing Systems, ICDCS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018
Y2 - 2 July 2018 through 5 July 2018
ER -