Next Article in Journal
StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
Previous Article in Journal
A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Future Internet 2018, 10(6), 53; https://doi.org/10.3390/fi10060053

A Privacy Preserving Framework for Worker’s Location in Spatial Crowdsourcing Based on Local Differential Privacy

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Received: 15 April 2018 / Revised: 16 May 2018 / Accepted: 13 June 2018 / Published: 14 June 2018
Full-Text   |   PDF [1076 KB, uploaded 14 June 2018]   |  

Abstract

With the development of the mobile Internet, location-based services are playing an important role in everyday life. As a new location-based service, Spatial Crowdsourcing (SC) involves collecting and analyzing environmental, social, and other spatiotemporal information of individuals, increasing convenience for users. In SC, users (called requesters) publish tasks and other users (called workers) are required to physically travel to specified locations to perform the tasks. However, with SC services, the workers have to disclose their locations to untrusted third parties, such as the Spatial Crowdsourcing Server (SC-server), which could pose a considerable threat to the privacy of workers. In this paper, we propose a new location privacy protection framework based on local difference privacy for spatial crowdsourcing, which does not require the participation of trusted third parties by adding noises locally to workers’ locations. The noisy locations of workers are submitted to the SC-server rather than the real locations. Therefore, the protection of workers’ locations is achieved. Experiments showed that this framework not only preserves the privacy of workers in SC, but also has modest overhead performance. View Full-Text
Keywords: spatial crowdsourcing; location privacy; local differential privacy spatial crowdsourcing; location privacy; local differential privacy
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Dai, J.; Qiao, K. A Privacy Preserving Framework for Worker’s Location in Spatial Crowdsourcing Based on Local Differential Privacy. Future Internet 2018, 10, 53.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top