Paper Info
Title
Private, Fair, and Verifiable Aggregate Statistics for Mobile Crowdsensing in Blockchain Era
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 fulfill 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.
Year
DOI
Venue
2020
10.1109/ICCC49849.2020.9238921
2020 IEEE/CIC International Conference on Communications in China (ICCC)
Keywords
DocType
ISSN
mobile crowdsensing,public blockchain,mobile users,private data values,crowdsensing server,FairCrowd,verifiable aggregate statistics,blockchain era,fair aggregate statistics,private aggregate statistics,greedy service provider,payment-escaping,payment-reduction,free-riding,double-reporting,Sybil attacks
Conference
2377-8644
ISBN
Citations 
PageRank 
978-1-7281-7328-3
0
0.34
References 
Authors
14
6
Name
Order
Citations
PageRank
Miao He184.89
Jianbing Ni2403.04
Dongxiao Liu300.68
Haomiao Yang400.34
Xuemin5102.17
Shen6303.28