Year: 2024
Type: article
Abstract: In the era of abundant location-based data, an increasing number of mobile users seek access to timely local information tailored to their individual interests. Consequently, the development of an efficient publish/subscribe system becomes pivotal, allowing a vast user base to seamlessly receive geo-textual objects based on their specific preferences from data streams. However, a notable challenge arises as mobile users often hesitate to disclose their personal interests, requirements, and locations to service providers in location-based publish/subscribe systems, giving rise to substantial data privacy concerns. In this light, we propose a privacy-preserving publish/subscribe framework, which not only facilitates real-time delivery of geo-textual objects to a large-scale audience of location-based subscribers, but also ensures the utmost privacy of subscribers’ locations and query keywords. Through experiments conducted on two real-life datasets, our proposed Privacy-Preserving Publish/Subscribe System demonstrates its capability to produce real-time matching results. The system can simultaneously handle millions of privacy-enhanced subscription queries over a stream of geo-textual objects.
Source: Data Intelligence
Authors: Ya Gao, Qiuyuan Ai, Jie Song
Institution: Peking University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Award: G9000112193
OpenAlex ID: W4401833884