Private, Fair, and Verifiable Aggregate Statistics for Mobile Crowdsensing in Blockchain Era

TL;DR

FairCrowd is a blockchain-based framework that ensures private, fair, and verifiable aggregate statistics in mobile crowdsensing, protecting user privacy and incentivizing honest participation despite malicious behaviors.

Contribution

The paper introduces FairCrowd, a novel system combining encryption and blockchain to achieve privacy, fairness, and verifiability in mobile crowdsensing aggregate statistics.

Findings

Achieves privacy preservation using ElGamal encryption.

Provides publicly verifiable correctness of aggregate statistics.

Ensures fairness in reward distribution despite malicious attacks.

Abstract

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.