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/subsc...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.展开更多
基金supported by National Natural Science Foundation of China under Grant No.72131001
文摘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.
