Leveraging Crowdsensed Data Streams to Discover and Sell Knowledge: A Secure and Efficient Realization
Chengjun Cai, Yifeng Zheng, Cong Wang · 2018
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.