Private, Fair, and Verifiable Aggregate Statistics for Mobile Crowdsensing in Blockchain Era
This addresses the challenge of secure and trustworthy data aggregation in mobile crowdsensing for customers and users, though it appears incremental by combining existing cryptographic and blockchain techniques.
The paper tackles the problem of ensuring privacy, fairness, and verifiability in aggregate statistics for mobile crowdsensing by proposing FairCrowd, a framework based on public blockchain and ElGamal encryption, which achieves verifiable aggregate statistics with privacy preservation and demonstrates high efficiency in experiments.
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.