  In this paper, we propose FairCrowd, a private, fair, and verifiable
framework for aggregate statistics in mobile crowdsensing based on the public
blockchain. In specific, mobile users are incentivized to collect and share
private data values (e.g., current locations) to fufill a commonly interested
task released by a customer, and the crowdsensing server computes aggregate
statistics over the values of mobile users (e.g., the most popular location)
for the customer. By utilizing the ElGamal encryption, the server learns nearly
nothing about the private data or the statistical result. The correctness of
aggregate statistics can be publicly verified by using a new efficient and
verifiable computation approach. Moreover, the fairness of incentive is
guaranteed based on the public blockchain in the presence of greedy service
provider, customers, and mobile users, who may launch payment-escaping,
payment-reduction, free-riding, double-reporting, and Sybil attacks to corrupt
reward distribution. Finally, FairCrowd is proved to achieve verifiable
aggregate statistics with privacy preservation for mobile users. Extensive
experiments are conducted to demonstrate the high efficiency of FairCrowd for
aggregate statistics in mobile crowdsensing.
