A differentially private method for crowdsourcing data submission

Abstract

Summary In recent years, the ubiquity of mobile devices has made spatial crowdsourcing a successful business platform for conducting spatiotemporal projects. In spatial crowdsourcing, workers contribute to a project by performing a task at a specific location. However, these platforms present serious threats to people's location privacy because sensitive information may be leaked from submitted spatiotemporal data. As a result, people may be hesitant to join spatial crowdsourcing projects, which hampers further applications of this business model. In this paper, we propose a private spatial crowdsourcing data submission algorithm, called PS‐Sub. This is a differentially private method that preserves people's location privacy and provides acceptable data utility. Rigorous privacy analyses theoretically demonstrate the privacy guarantees inherent in the proposed model. Experiments based on real‐world datasets were conducted using practical evaluation metrics. The results show that our method is able to achieve location privacy preservation efficiently, at an acceptable cost for spatial crowdsourcing applications.

Editorial: Recent advances in machine learning for cybersecurity

“…More importantly, the location information combined with timestamps has additional power and value because it can be used to track individuals. Zhang et al 8 For more technical details, readers are strongly recommended to read the papers included in this special issue.…”

“…More importantly, the location information combined with timestamps has additional power and value because it can be used to track individuals. Zhang et al find a unique scenario of protecting the privacy of spatiotemporal data in the context of crowdsourcing workers. That is, in a spatial crowdsourcing project, workers need to complete a task at a specific location and then share the location as a proof of work.…”

“…That is, in a spatial crowdsourcing project, workers need to complete a task at a specific location and then share the location as a proof of work. Zhang et al identify the privacy issues of many platforms for crowdsourcing workers. To protect the location privacy of these workers, Zhang set al propose a private spatial crowdsourcing data submission algorithm, namely, PS‐Sub .…”

“…Zhang et al identify the privacy issues of many platforms for crowdsourcing workers. To protect the location privacy of these workers, Zhang set al propose a private spatial crowdsourcing data submission algorithm, namely, PS‐Sub . The PS‐Sub algorithm uses a differential privacy method to preserve workers' location privacy with the provision of an acceptable level of data utility.…”