Name
Abstract | ||
|---|---|---|
Providing appropriate monetary incentives for participants is vital for crowdsensing to encourage their participation. Among all outstanding incentive mechanisms, posted pricing has been widely adopted because it is easy to implement and naturally achieves truthfulness and fairness. However, existing schemes either lack of privacy protection for the sensing data of participants, or fail to consider the diversity of sensing quality in crowdsensing systems. To address these critical problems, we propose a privacy-preserving dynamic pricing mechanism for robust crowdsensing, which only needs to spend a small amount of total payments to recruit a group of mobile users with reasonable sensing quality while protecting the sensing data privacy of each participant. Specifically, we first design an efficient secure aggregation algorithm through which the platform can compute the sum of sensing data from participants without learning each participant's individual data. Then, we employ the aggregation algorithm to design a secure quality assessment algorithm to obtain the sensing quality levels of participants. Finally, according to the varying quality levels, we develop a model free reinforcement learning based approach to optimize pricing policy to achieve lower total payments and robustness requirement. Through privacy analysis and extensive experiments, we demonstrate the effectiveness and efficiency of the proposed mechanism. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.comnet.2020.107582 | COMPUTER NETWORKS |
Keywords | DocType | Volume |
Crowdsensing, Privacy, Posted pricing, Data quality | Journal | 183 |
ISSN | Citations | PageRank |
1389-1286 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxian Liu | 1 | 5 | 1.40 |
| Fagui Liu | 2 | 23 | 6.06 |
| Hao-Tian Wu | 3 | 13 | 4.99 |
| Xinglin Zhang | 4 | 41 | 7.02 |
| Bowen Zhao | 5 | 14 | 6.32 |
| Xingfu Yan | 6 | 2 | 1.71 |