Literature Database Entry
wang2022privacy
Hengzhi Wang, En Wang, Yongjian Yang, Jie Wu and Falko Dressler, "Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing: A Graph-based Approach," Proceedings of 41st IEEE International Conference on Computer Communications (INFOCOM 2022), Virtual Conference, May 2022, pp. 570–579.
Abstract
Recently, the growing popularity of Spatial Crowd-sourcing (SC), allowing untrusted platforms to obtain a great quantity of information about workers and tasks' locations, has raised numerous privacy concerns. In this paper, we investigate the privacy-preserving task assignment in the online scenario, where workers and tasks arrive at the platform in real time and tasks should be assigned to workers immediately. Traditional online task assignments usually make a benchmark to decide the following task assignment. However, when location privacy is considered, the benchmark does not work anymore. Hence, how to assign tasks in real time based on workers and tasks' obfuscated locations is a challenging problem. Especially when many tasks could be assigned to one worker, path planning should be considered, making the assignment more challenging. To this end, we propose a Planar Laplace distribution based Privacy mechanism (PLP) to obfuscate real locations of workers and tasks, where the obfuscation does not change the ranking of these locations' relative distances. Furthermore, we design a Threshold-based Online task Assignment mechanism (TOA), which could deal with the one-worker-many-tasks assignment and achieve a satisfactory competitive ratio. Simulations based on two real-world datasets show that the proposed algorithm consistently outperforms the state-of-the-art approach.
Quick access
Original Version (at publishers web site)
Authors' Version (PDF on this web site)
BibTeX
Contact
Hengzhi Wang
En Wang
Yongjian Yang
Jie Wu
Falko Dressler
BibTeX reference
@inproceedings{wang2022privacy,
author = {Wang, Hengzhi and Wang, En and Yang, Yongjian and Wu, Jie and Dressler, Falko},
doi = {10.1109/INFOCOM48880.2022.9796827},
title = {{Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing: A Graph-based Approach}},
pages = {570--579},
publisher = {IEEE},
address = {Virtual Conference},
booktitle = {41st IEEE International Conference on Computer Communications (INFOCOM 2022)},
month = {5},
year = {2022},
}
Copyright notice
Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.
The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.
The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.
This page was automatically generated using BibDB and bib2web.