An increasing number of applications and functions are being introduced.into smartphones, but smartphones have limited computation ability and battery resources. To enhance smartphone capacity, cloud computing and vir...An increasing number of applications and functions are being introduced.into smartphones, but smartphones have limited computation ability and battery resources. To enhance smartphone capacity, cloud computing and virtualization techniques can be used to shift the workload from smartphone to computational infrastructure. In this paper, we propose a new framework in which a mirror is kept for each smartphone on a computing infrastructure in the telecom network. With mirrors, the workload can be greatly reduced, and smartphone resources can be virtually expanded. The feasibility of deploying this framework in telecom networks is demonstrated in the protocol design, a synchronization study, and a scalability test. Two applications are introduced to show how computational workload on the smartphone and traffic in the telecom network are signiScantly reduced using our techniques.展开更多
Ride-hailing service has become a popular means of transportation due to its convenience and low cost.However,it also raises privacy concerns.Since riders’mobility information including the pick-up and drop-off locat...Ride-hailing service has become a popular means of transportation due to its convenience and low cost.However,it also raises privacy concerns.Since riders’mobility information including the pick-up and drop-off location is tracked,the service provider can infer sensitive information about the riders such as where they live and work.To address these concerns,we propose location privacy preserving techniques that efficiently match riders and drivers while preserving riders’location privacy.We first propose a baseline solution that allows a rider to select the driver who is the closest to his pick-up location.However,with some side information,the service provider can launch location inference attacks.To overcome these attacks,we propose an enhanced scheme that allows a rider to specify his privacy preference.Novel techniques are designed to preserve rider’s personalized privacy with limited loss of matching accuracy.Through trace-driven simulations,we compare our enhanced privacy preserving solution to existing work.Evaluation results show that our solution provides much better ride matching results that are close to the optimal solution,while preserving personalized location privacy for riders.展开更多
文摘An increasing number of applications and functions are being introduced.into smartphones, but smartphones have limited computation ability and battery resources. To enhance smartphone capacity, cloud computing and virtualization techniques can be used to shift the workload from smartphone to computational infrastructure. In this paper, we propose a new framework in which a mirror is kept for each smartphone on a computing infrastructure in the telecom network. With mirrors, the workload can be greatly reduced, and smartphone resources can be virtually expanded. The feasibility of deploying this framework in telecom networks is demonstrated in the protocol design, a synchronization study, and a scalability test. Two applications are introduced to show how computational workload on the smartphone and traffic in the telecom network are signiScantly reduced using our techniques.
文摘Ride-hailing service has become a popular means of transportation due to its convenience and low cost.However,it also raises privacy concerns.Since riders’mobility information including the pick-up and drop-off location is tracked,the service provider can infer sensitive information about the riders such as where they live and work.To address these concerns,we propose location privacy preserving techniques that efficiently match riders and drivers while preserving riders’location privacy.We first propose a baseline solution that allows a rider to select the driver who is the closest to his pick-up location.However,with some side information,the service provider can launch location inference attacks.To overcome these attacks,we propose an enhanced scheme that allows a rider to specify his privacy preference.Novel techniques are designed to preserve rider’s personalized privacy with limited loss of matching accuracy.Through trace-driven simulations,we compare our enhanced privacy preserving solution to existing work.Evaluation results show that our solution provides much better ride matching results that are close to the optimal solution,while preserving personalized location privacy for riders.
